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Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions. Significance Statement This study illustrates a novel method based on machine learning for reducing the systematic surface temperature errors that characterize multiple atmospheric reanalyses in sea ice–covered regions of the Arctic under clear-sky conditions. The correction applied to the temperature field is consistent with the local weather and the sea ice and snow conditions, meaning that it responds to seasonal changes in sea ice cover as well as to its long-term decline due to global warming. The corrected reanalysis temperature can be employed to support polar research activities, and in particular to better simulate the evolution of the interacting sea ice and ocean system within numerical models.

Global coupled simulations that can resolve atmospheric storms and mesoscale oceanic features at the kilometre-scale have recently become possible to run over short time slices, for example on a seasonal timescale. Here we give an overview of the first multi-year simulations performed with ECMWF’s Integrated Forecasting System (IFS), coupled to both the NEMO and FESOM2 ocean-sea ice models, for the H2020 Next Generation Earth Modelling Systems (nextGEMS) project. The project aims to build a new generation of eddy- and storm-resolving global coupled Earth System Models. Along with ICON, the other model participating in nextGEMS, the IFS-based models form the basis also for Digital Climate Twins of Earth as envisioned in the European Union’s ambitious Destination Earth project. nextGEMS relies on several model development cycles, in which the models are run and improved based on community feedback. In an initial set of storm-resolving coupled simulations (Cycle 1), the IFS was integrated for 75 days. For Cycle 2, IFS has been run at the operational 9 km resolution as a baseline, and at 4.4 km and 2.8 km global spatial resolution for up to 1 year of simulation (4.4 km). To our knowledge, the 8-months long 2.8 km simulation in Cycle 2 represents the first fully coupled simulation ever of this duration at this high level of spatial detail and is made available to the public. The runs at 9 km were performed with the parameterization for deep convection active as in the operational system, while at 4.4 km and 2.8 km, separate experiments with IFS were run both with and without the deep convection parameterization.We document the model improvements made to IFS-FESOM/NEMO based on the lessons learned from the first Cycle 1 runs, which were included for the second round of Cycle 2 simulations; these mainly consist in vastly improved conservation properties of the coupled model systems in terms of water and energy balance, which are crucial for longer climate integrations, and in a more realistic representation of the snow and surface drag. Cycle 2 also targeted eddy-resolving resolution in large parts of the mid- and high-latitude ocean (better than 5km) to resolve mesoscale eddies and linear kinematic features (i.e. leads or cracks) in sea ice. For IFS-FESOM, this is made possible thanks to a recently refactored ocean model code that can be linked as an external library and that allows for efficient coupled simulations in the single-executable context with IFS, via hybrid parallelization with MPI and OpenMP.

Accurate ocean initial conditions are beneficial to coupled ocean - atmosphere forecasts in several ways depending on context. On short time scale (days), some extreme, societally high-impacting meteorological events such as tropical cyclones are associated with exceptionally intense air-sea exchanges, thus requiring good knowledge of the initial state of the upper ocean layers. As the forecast evaluation time scale gets longer (seasonal, decadal), the information contained within the atmospheric initial conditions becomes virtually ineffective to the advantage of that contained within the ocean’s.In phase one of the Destination Earth (DestinE) initiative of the European Union, ECMWF is responsible for delivering the first two digital twins, on weather extremes and climate change adaptation. These will rely on the fusion of observations and cutting-edge, high-resolution versions of Earth system models. Developing a cheap, affordable method for generating realistic high-resolution ocean initial conditions is particularly critical to both these forthcoming digital twins developed in DestinE. In this presentation, we thus introduce a new ocean initial condition generation method built to meet these specific needs.This method consists in running preliminary nudged ocean-standalone experiments, using the NEMO ocean model, which is part of the ECMWF’s Integrated Forecasting System. Its main technical novelty is the adaptation of a former sea-ice nudging scheme to NEMO’s multicategory SI3 sea-ice model, and it can be significantly cheaper than relying on ocean data assimilation, particularly at high resolutions. However, this method touches on several matters related to the ability of ocean models to be diverted from their natural equilibrium by being constrained towards increasingly realistic states. We investigate the criteria for obtaining realistic ocean initial condition using distinct combinations of model and nudging dataset resolutions, and evaluate the resulting skill of coupled ocean - atmosphere forecasts.

The drive to develop environmental prediction systems that are seamless across both weather and climate timescales has culminated in the development and use of Earth system models, which include a coupled representation of the atmosphere, land, ocean and sea ice, for medium-range weather forecasts. One region where such a coupled Earth system approach has the potential to significantly influence the skill of weather forecasts is in the polar and sub-polar seas, where fluxes of heat, moisture and momentum are strongly influenced by the position of the sea ice edge. In this study we demonstrate that using a dynamically coupled ocean and sea ice model in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System results in improved sea ice edge position forecasts in the Northern Hemisphere in the medium range. Further, this improves forecasts of boundary layer temperature and humidity downstream of the sea ice edge in some regions during periods of rapid change in the sea ice, compared to forecasts in which the sea surface temperature anomalies and sea ice concentration do not evolve throughout the forecasts. However, challenges remain, such as large errors in the position of the ice edge in the ocean analysis used to initialise the ocean component of the coupled system, which has an error of approximately 50 % of the total forecast error at day 9, suggesting there is much skill to be gained by improving the ocean analysis at and around the sea ice edge. The importance of the choice of sea ice analysis for verification is also highlighted, with a call for more guidance on the suitability of satellite sea ice products to verify forecasts on daily to weekly timescales and on meso-scales (< 500 km).

Carbonate reservoirs require effective acid stimulation to improve well productivity. For long horizontal wells, a complicating factor has previously been the difficulty of controlling acid placement along the reservoir section. The Smart Liner (SL) concept solves this problem. It consists of a number of small holes spaced in such a way so as to distribute acid evenly along the reservoir interval. Fig. 1 shows a schematic of the concept. Without the need of a coiled tubing, acid is bull-headed at a high rate from surface through the production tubing and enters the liner from the left. The liner does not have to be horizontal but very often is. When acid reaches the first hole, which typically has a diameter of 3–6 mm, the pressure drop across the hole is so large that only a small portion of the acid exits the liner through the hole; the remaining acid continues along the liner until it reaches the next hole where the process is repeated. Appropriate hole spacing ensures that acid is distributed with a given acid coverage (dosage) in barrels of acid per foot of reservoir section. The small cross-sectional area of the holes results in focused acid jetting at velocities often exceeding 20 m/s (65 ft/s). The jetting helps promote wormhole formation leading to substantial productivity enhancement. The hole-spacing design requires dedicated software which must take into consideration constraints pertaining to the reservoir, the well, and the pumping equipment while minimizing operational complexity. The tool should also yield a quick answer to enable the user to optimize the final design post drilling and provide a running tally consisting of the sequence of liner joints to be run in hole. In heterogeneous reservoirs, it is typically required to segment the wellbore with swellable packers to isolate sections with different reservoir pressure and/or fluid mobility, and the SL concept readily accommodates that. Also, since the subsequent stimulation relies on matrix acidization, the tool must ensure that fracturing pressures are not exceeded. The original limited-entry liner (LEL) technique dates to the early 1960s and was proposed for fracturing applications (SPE 530). Reviews by Somanchi et al. (SPE 184834) and Weddle et al. (SPE 189880) suggest that it is still widely applied for such purposes. In the late 1990s, Maersk Oil adapted the concept to effectively stimulate extended-reach wells in its North Sea chalk reservoirs on a large scale. Hansen and Nederveen (SPE 78318) refer to the technology as controlled acid jetting (CAJ). In the following years, the CAJ technique was implemented in more difficult formations such as a 0.1 mD chalk reservoir (SPE 144159) as well as in a tight, gas-bearing formation (SPE 123979). Balsawer et al. (IPTC 17611) designed multizone completions in ultralong wells in a giant field offshore Qatar, which comprised more heterogeneous limestone formations. Other field implementations have been described by Issa et al. (SPE 171779) for a super-giant reservoir offshore Abu Dhabi. Past deployments have concentrated on extended-reach wells with reservoir temperatures up to 210°F. Over the past year, ADNOC has significantly expanded the operational envelope of the Smart Liner concept. Fig. 2 summarizes the variation in completed reservoir length and average permeability for 80 well designs. Table 1 shows that reservoir temperatures vary from 140°F to 300°F.

Optimum productivity from long horizontal wells requires effective stimulation. The Smart Liner lower completion addresses shortcomings of other matrix-acid stimulation techniques in regards to acid placement along the reservoir section. The concept relies on a limited number of small holes (orifices) drilled through the liner itself to distribute acid over the entire drain length. Different hole spacings can yield similar flow distributions but may result in different stimulation efficiencies. What is best, a few deep wormholes with acid washing in between, or many, less penetrating but closer wormholes? The objective of this work is to investigate which parameters influence the optimum hole spacing leading to the most improved overall reservoir contact.We derive a radial wormhole growth model as a function of time and initial jet velocity, which honors the observation that incremental penetration depth tapers off versus time. This model is specifically tailored to account for the substantial jet velocities generated by the limited-entry liner completion, often in the range of 15-50 m/s, occasionally higher. It qualitatively matches field observations showing that the maximum required acid coverage often lies in the range 0.4-0.5 bbl/ft. Exceptions to this trend have been observed and they deserve further investigation. The hole spacing generally increases with well length to ensure sufficient jetting towards the toe of the well.Work is ongoing to convert the number and length of all wormholes combined to an average overall injectivity improvement for the well. This effort will lead to a more qualitative match to the real-time stimulation data. Field data suggest that the typical transient injectivity is improved by a factor 2-3, sometimes more, if the initial skin prior to stimulation is very high. Larger improvements have been observed during stimulation of dolomite rocks with 28 wt% HCl, possibly due to more drilling-induced damage.

Asphaltene precipitation can sometimes pose operational problems in medium-light oils because of the low asphaltene solubility. The purpose of this work is to develop a methodology to predict the asphaltene envelope for all fluid systems in ADNOC’s reservoir portfolio based on existing laboratory experiments. Such a model would then be able to predict potential precipitation risks for current and future field development projects, especially the ones involving gas injection. The starting point for development of the predictive model for asphaltene onset pressure (AOP) is the 100+ lab measurements carried out over several decades, of which 65% involve a number of injection gases such as CO2, lean and rich hydrocarbon gas, as well as sour gas. We then matched each data set with an equation of state (EOS) to generate the entire onset pressure envelope. Based on the envelope data points, we applied a data-driven method to reproduce the key trends, and used this trained model as a novel predictive tool for new production scenarios without experimental AOP data. We first tested the PC-SAFT model for our phase behavior calculations but found that the method, as implemented in the software package, often experienced convergence problems. The PR-78 cubic EOS was found to be more reliable with the ability to match the experimental data despite limited predictive power. We find that availability of AOP data for reservoir fluids swollen with injection gas makes the thermodynamic model much more robust compared to tuning to a few AOP data on the original reservoir fluid alone. A single AOP point is generally not sufficient to fully constrain the EOS model unless model parameters from other studies are brought into use. SARA analysis is not mandatory for the EOS tuning itself and was found not to be required for training any of the data-driven methods. We limited the predicted data sets to temperatures below 350 °F, since all our reservoirs have temperatures below this threshold. From the calculated envelopes, we saw a clear impact of fluid composition on the shape of the AOP curve relative to the saturation pressure curve, as expected. We now have a tool, which can accurately predict the AOP curve for a combination of reservoir fluids and injection gases, as the long as the injection gas composition remains within the range tested experimentally.

Unit R is a tight carbonate reservoir onshore Abu Dhabi currently undergoing redevelopment with the aim to substantially improve the ultimate recovery factor. Better pressure support is required to increase the production. To this end, two water injectors were drilled in tight areas where wells suffer from low injectivity index and high well head injection pressure (WHIP). Reaching the expected injection rates therefore required very aggressive matrix-acid stimulation techniques. Acid wash is a common stimulation practice for medium-high permeability wells in ADNOC when the purpose is merely to remove drilling damage without creating large negative skins. For low permeability reservoirs such as Unit R, more aggressive stimulation techniques are needed to deliver the expected production and injection target rates. We tested two such methods; Well A1 was treated using Abrasive Jetting (JA), which is conveyed by coiled tubing whereas in Well A2, we deployed a Smart Liner (SL) where acid can be bullheaded at high rate from surface. In Well A1, the Abrasive Jet CTU in Open Hole doubled the injectivity index. However, the Injection Logging Test (ILT) revealed an uneven injection profile along the horizontal section (more injection in the better properties zone). In well A2, Smart Liner bull-head acid stimulation was able to triple the injectivity index. The ILT shown an even injection profile across the horizontal section, actually slightly more in areas with poor rock properties due to a large number of holes in those intervals. The cost of the Abrasive Jet is 40% less than the Smart Liner stimulation but the latter has more medium/long term advantages: Even injection along horizontal section (improving sweep efficiency) Ensure 100% accessibility to the well (avoiding formation collapse issue) Stimulation from surface, no need CTU (saving cost), useful for potential future re-stimulations. After only 10 months of injection the reservoir pressure has increased 50-100 psi in surrounding nearby oil producers, resulting in 20% more oil while honoring all operational guidelines. It is quite exceptional to be able to assess with real data the effectiveness of two different stimulation techniques. The two pilot wells have same horizontal length, almost same rock properties, and are drilled parallel to each other. Both Smart Liner and abrasive jetting substantially improve water injectivity but the more uniform flow profile observed in the Smart Liner is a distinct advantage when selecting an appropriate completion-stimulation design for future water injectors.

Gas injection pressure-volume-temperature (PVT) laboratory data play an important role in assessing the efficiency of enhanced oil recovery (EOR) processes. Although typically there is a large conventional PVT data set, gas injection laboratory studies are relatively scarce. On the other hand, performing EOR laboratory studies may be either unnecessary in the case of EOR screening, or unfeasible in the case when reservoir fluid composition at current conditions is different from initial conditions. Given that gas injection is to be widely assessed as an optimal EOR process, there is increased demand on time- and cost-effective solutions to predict the outcome of associated gas injection lab experiments. While machine learning (ML) is extensively used to predict black-oil properties, it is not the case for compositional reservoir properties, including those related to gas injection. Can we use the typically extensive conventional laboratory data to help predict the needed gas injection parameters? This is the core of this paper. We present an ML-based solution that predicts pertinent gas injection studies from known fluid properties such as fluid composition and black oil properties. That is, learning from samples with gas injection laboratory studies and predicting gas injection fluid parameters for the remaining, much larger, data set. We applied the proposed algorithms on an extensive corporate-wide database. Swelling tests were predicted using the trained ML models for samples lacking gas injection laboratory data. Several ML models were tested, and results were analyzed to select the most optimal one. We present the algorithms and the associated results. We discuss associated challenges and applicability of the proposed models for other fields and data sets.

Developing mature reservoirs is associated with challenges and limitations both in surface and subsurface. In This paper we will tackle the area of developing reservoirs with Maximum Reservoir Contact (MRC) wells by introducing our experience in combining it with LEL completion and introduce initial results in unlocking the potential of our reservoir with reduced capital expenditure (CAPEX). Conventional wells with 3000 ft open hole length become more challenging as development of the field progresses and especially with drilling new wells. Congestion and anti-collision at surface and subsurface limitations arise more often let alone the cost, manpower and environmental issues. We are going to introduce Maximum reservoir contact MRC wells with extended open hole length of around 10,000 ft. which allow to maximize gain from this well while optimizing other parameters (well spacing, cost…etc.). Measurement of rate and pressure, pre and post acid stimulation controlled by LEL completion as well as comparison with conventional water injectors in the vicinity were performed before to evaluate the performance of the MRC well. It was confirmed that MRC well injectivity surpasses the conventional injectors with shorter open hole length in relatively tight areas of the reservoir which have low permeability especially if equipped with LEL completion. Initial results show Injectivity improvement compared with nearby water injectors plus additional gain in injectivity noticed after acid stimulation with the LEL completion. The MRC well performance after stimulation showed well head injection pressure decreased drastically after the acid stimulation with increased injected volumes of water. This shows that smart completion solutions well help to improve performance and optimize acid stimulation which highlights the importance of combining MRC with LEL to maximize gain and enhance performance to take the maximum benefit. Through our success with MRC well with LEL completion the community of petroleum engineering will be able to take decisions regarding implementing this method and technology to optimize their drilling which will have positive impact in cost, planning and environment.

Integrated asset models are being constructed for all ADNOC assets as part of a production optimization initiative supported by a significant digitization effort. Contrary to the standard industry practice of utilizing black-oil formulations to capture fluid behavior, a compositional modeling framework was selected to address some key challenges: Compositional variation at reservoir-level, either lateral or verticalInjection of gas (immiscible as well as near-miscible) causing mass transferBlending of different fluids in the surface network at line conditionsOperational requirement to maintain the bottom-hole pressure above saturation pressureValidation of raw well test data before shrinkage correction (line conditions)Investigation of impact of changing separator settings (affecting shrinkage correction)Tracking of fluid composition in produced streams The compositional framework is very comprehensive. For each of the 100+ producing reservoirs, one or several equation of state (EOS) models was developed. In every well, an initial fluid composition estimate was provided as an anchoring point, which was subsequently adjusted in a tailor-made workflow to match the solution gas-oil ratio measured in the field by performing an isothermal flash of the original composition and then recombining the flashed oil and gas streams to the field gas-oil ratio. The workflow offers a number of advantages, one of which is that the path-to-surface correction can be imposed directly on field measurements. This has resulted in much improved allocation factors for oil, gas, and water. Fit-for-purpose algorithms have been developed to perform well test validation for different well types based on raw data at line conditions.

Smart Liners are based on the limited-entry liner principle, which consist of an uneven spacing of a limited number of small holes drilled through the liner to mechanically divert acid along the entire reservoir drain. Past deployments have concentrated on extended-reach wells for which no other efficient matrix-acid stimulation techniques exist. With more than 100 deployments last year, ADNOC has significantly expanded the operational envelope in terms of pump rate, hole size, and well length. Results from the Smart Liner implementation have been very encouraging so far. The general observation is that long-term productivity or injectivity is doubled, sometimes tripled, and the entire wellbore contributes to flow, documented by pre- and post-stimulation production logging. The technique has been successfully applied to several oil producers and water injectors, as well as to one gas producer, and yields superior well performance also in situations where conventional techniques can be deployed. A number of points are worth highlighting: The technique is well-suited for both short and extended-reach wells; however, hole spacing designs strongly depend on the well lengthThe technique works for carbonate rocks of different lithology, such as limestone and dolomite (in addition to numerous chalk applications documented in the literature)Heterogeneity can to some extent be mitigated by appropriate placement of swellable packers and adjustment of hole density prior to stimulationThe high jet velocity through the small holes provides the right initial conditions for wormhole propagationFor water injectors, a pre-stimulation baseline injection period that cools the near-wellbore region in high-temperature reservoirs may improve the wormhole propagation. The rapid deployment of this technique as well as the scale of implementation is unique in the industry.

Matrix-acid stimulation is a key production enhancement technique for carbonate reservoirs. The ability to control acid placement by diversion along the reservoir section is often challenging, particularly for heterogeneous formations where low-permeability intervals may remain under-stimulated. In this paper, we demonstrate via subsequent inflow profiling that efficient stimulation of both low- and high-permeability segments can be achieved using a completion method called a Smart Liner and a new methodology of downhole flow monitoring without operational well intervention by utilizing inline tracer system across the lower completion.The Smart Liner is a completion technique, which relies on a number of small and unevenly spaced holes to divert acid along a horizontal drain, while incorporating swellable packers to isolate segments with different reservoir properties. The hole spacing design is unique for each well and requires a software algorithm to balance the outflow. The inflow monitoring is assessed through both inline chemical tracers and conventional PLT. Main components of this algorithm has been presented in an earlier publication.The inline tracer system can provide "wireless" continuous production profiling without the risk & cost associated with conventional PLT methods. The inline tracer samples were collected during both stages of pre-stimulation and post-stimulation activities on oil producer wells, which shows downhole contribution of all segments, acid arrival and enhancement of Mid section of the reservoir drain. Moreover, PLT was conducted to validate inline tracer results.Past deployments of Smart Liners have targeted homogeneous reservoir intervals, but in this paper, we show that also reservoirs with significant permeability variation can be effectively matrix-acid stimulated. It is to our knowledge the first time that inflow profiling of a long horizontal well has been probed with inline tracers as well as PLT. Furthermore, we quantify the incremental impact of a given acid volume on injectivity based on the high-frequency data from the stimulation job.

First-contact miscibility (FCM) occurs when an original injection gas is miscible with a reservoir oil regardless of the mixing ratio. As a consequence, the displacement will remain single-phase and the microscopic displacement efficiency will be 100%. For the vast majority of field applications, FCM requires a pressure, which is prohibitively high. Some notable exceptions include ultra-sour fluids, such as the fluid system encountered in this reservoir, offshore Abu Dhabi. The exceptionally favorable miscibility conditions may help unlock further value from this challenging, ultra-sour, high-temperature, offshore field. The swelling experiment provides a key source of information for inferring the FCM pressure. The swelling pressure curve exhibits a maximum versus the volume of injected gas and this maximum pressure is the FCM pressure. Typically, most lab experiments are terminated once the transition point from a bubble-point system to a dew-point system is observed, but for this application, much larger gas volumes were added to the system to reduce uncertainty on the inflection point. We performed equation of state (EOS) predictions to guide the experimental design in terms of the selection of gas additions. We then used the EOS formulation to conduct numerical simulations to compare the efficiency of a first-contact miscible displacement against a multi-contact miscible displacement, which can take place at lower pressures. With numerical simulation, we show that a first-contact miscible displacement yields higher recovery compared to a multi-contact miscible displacement. We attribute this difference to the development of the compositional transition zone observed during multi-contact miscible displacement which gives rise to a gas-oil interfacial tension and a bending of the gas-oil relative permeability curves for that zone. The impact of IFT on relative permeability would depend on model parameters in the simulator. The gas utilization factor, however, reaches its minimum if gas re-injection is delayed by a couple of years, as a result of the impact of pressure on formation volume factor of reservoir oil and injection gas, respectively.

Flash calculation is an essential step in compositional reservoir simulation. However, it consumes a significant part of the simulation process, leading to long runtimes that may jeopardize on-time decisions. This is especially obvious in large reservoirs with many wells. In this paper we describe the use of a machine-learning- (ML) based flash-calculation model as a novel approach for novel thermodynamics via this ML framework to potentially accelerate compositional reservoir simulation. The hybrid compositional simulation protocol uses an artificial-intelligence- (AI) based flash model as an alternative to a thermodynamic-based phase behavior of hydrocarbon fluid, while fluid-flow equations in the porous medium are handled using a conventional approach. The ML model capable of performing accurate flash calculations is integrated into a reservoir simulator. Because flash calculations are time consuming, this can lead to instability issues; using the ML algorithm to replace this step results in a faster runtime and enhanced stability. The initial stage in training ML models consists of creating a synthetic flash data set with a wide range of composition and pressure. An automated workflow is developed to build a large flash data set that mimics the fluid behavior and pressure depletion in the reservoir using one or more fluid samples in a large pressure-volume-temperature (PVT) database. For each sample, a customized equation of state (EOS) is built based on which constant volume depletion (CVD) or differential liberation (DL) is modeled with prescribed pressure steps. For each pressure step, a constant composition expansion (CCE) is modeled for the hydrocarbon liquid with, in turn, prescribed pressure steps. For each of the CVD and multiple CCEs steps, flash calculation is performed and stored to build the synthetic database. Using the automatically generated flash data set, ML models were trained to predict the flash outputs using feed composition and pressure. The trained ML models are then integrated with the reservoir simulator to replace the conventional flash calculations by the ML-flash calculation model, which results in a faster runtime and enhanced stability. We applied the proposed algorithms on an extensive corporate-wide database. Flash results were predicted using the ML algorithm while preceded by a stability check that is performed using another ML model tapping into the exceptionally large PVT database. Several ML models were tested, and results were analyzed to select the most optimal one leading to the least error. We present the ML-based stability check and flash results together with results illustrating the performance of the reservoir simulator with integrated AI-based flash, as well as a comparison to conventional flash calculation. We are presenting a comprehensive AI-based stability check and flash calculation module as a fully reliable alternative to thermodynamic-based phase behavior modeling of hydrocarbon fluids and, consequently, a full integration to an industry-standard reservoir simulator.

Development of giant offshore carbonate field with more than 400 oil producers and 300 water injectors are very complex without proper production optimization tool to reach sustainable production and optimize the cost of the investment for future development. Construction of IAM model for short term optimization purposes brings vital outcomes which can identify the short and long term optimizations scenarios and as a consequences reduce the investment with the maximum outcome of production within the reservoir management guidelines of the field. Integrated asset modelling tool (PSO-production system optimization tool Prosper+Gap) is ideal tool which can provide the best production optimization results to develop the giant field with all reservoir guidelines and production system bottleneck. One of the main achievement of integrated asset model (PSO) for a giant carbonate oil field is the fact that its fully compositional model which allows to track oil composition change at any point of the surface facility, which is critical for fields under secondary recovery mechanisms with water and gas injection.

One of the main challenges in the oil industry is to develop tighter carbonate reservoirs. There are several methods to improve oil recovery (IOR), the adoption of Novel Lower Completions in horizontal wells can be one of the best tools to maximize reservoir contact and to control unwanted fluids (i.e., water or gas). It is especially important in carbonate reservoirs where most of the time it is imperative to provide additional stimulation in order to produce the reservoirs economically. The main objective of the presented work is to provide an alternative type of completion that creates better flow and stimulation distribution along the horizontal hole, In addition, the movable parts were eliminated to reduce the integrity risk while injecting the acid job stimulation. In carbonate reservoirs, the standard method to improve reservoir permeability is through acid stimulation utilizing coiled tubing to place acid into the reservoir rock and improve the flow within the near wellbore area. This technique is costly and time consuming. Furthermore, in the specific case presented, there is the constraint of limited number of coiled tubing units where many times acid stimulation is delayed due to this fact. Installing the mono-bore segmented completion provides the capability to perform the job without the rig, only bull heading the acid utilizing high pressure pumps. There are several options to install lower completions in the oil industry for horizontal wells. Smart liner is presented as an alternative to the conventional pre-perforated liner. The smart liner design was done with a proprietary software and consists of a specific number of small holes distributed along the horizontal section to distribute the flow and provide better stimulation placement at higher rates improving "worm holes" creation and extension. The lower completion is equipped with swellable packers to provide segmentation in the horizontal hole for future requirements if needed such as zones shut off. After the successful installation in a water injection well, water injection was initiated to define a baseline to compare with the results after the acid stimulation. The paper presents the lower completion design, the acid stimulation program and the results before and after the stimulation is done without rig on site. Initial results after the stimulation are showing more than twice the injection rate after the job. This job was done without the use of coiled tubing providing a better acid stimulation along the horizontal hole. The 4-½ inches mono-bore completion is the first time that was done in the Asset, creating new opportunities to improve the stimulation and injectivity / productivity of the wells and consequent future Field development.

This paper presents a summary of the deployed Smart Liner- SL equivalent to the Limited Entry Liner- LEL as lower completion for the first time in a sidetracked Gas well, Offshore Abu Dhabi. R-1 is subdivided into several sub-layers, the reservoir properties are characterized by low porosity & low permeability (Tight). Reservoir quality in the Upper part is better in terms of porosity & permeability than the lower part. The gas production is mainly from top part of R-1 reservoir, no contribution from Lower part. In 2017, Data gathering was conducted on well A-1 (Coring, Logging & Pressure Points). Actual Gas production Offset wells are restricted from optimal production due to Well Integrity Sustainable Annulus Pressure, to compensate the restricted aged wells due to Well Integrity, Gas production can be increased to 3 times using SL as a stimulation method.The Smart Liner was selected as a lower completion and as a stimulation method for better flow distribution, improved well performance, effective Acid stimulation, also to ensure hole accessibility, allowing aggressive bullhead stimulation at high rate/pressure and high acid concentration at less time ~ 1.5days/job, in addition to eliminating high risk and high cost Coiled Tubing (CT) intervention for stimulation.The first step was to design the SL Completion Workflow with a representative well trajectory for the selected well to be fed and reservoir properties to be extracted from the dynamic model, and then to create a representative stimulation model utilizing property numerical software with all possible scenarios; open hole that represents PPL and suggested SL compartmentalization and holes distribution based on reservoir parameters along the lateral. Once the well model is created, different scenarios for different completion designs are to be run versus different acid concentrations and volumes till achieving the optimum results from stimulation point of view in addition to formation and facilities limitations. Drilling operations were very challenging; fortunately, we succeed to deploy the SL after final adjustment based on FMI Natural Fractures. The Smart Liner as stimulation has proved to be a cost-effective solution for gas wells comparing to advanced stimulation methods in addition to eliminating the high risk and high cost of the Coiled Tubing (CT) intervention for stimulation a huge savings in well construction with maximizing performance.

In a giant, mature UAE offshore field, consisting of complex multi-stacked heterogeneous reservoirs, the western part has been less developed, due to contrasted reservoir properties and low-permeability layers. The development in that part of the field was re-visited, to account for reservoir challenges and surface limitations. The objective was to achieve production mandates, understand reservoir behavior, while minimizing well count and expenditures associated with interventions and surveillance activities.To evaluate this challenging area of the field, a unique multi-lateral well was designed, targeting three distinct reservoirs, and allowing to concurrently produce and understand them in a viable manner. The reservoirs have poor characteristics, with permeability lower than 10 mD, except for the deeper one, which has some high permeability streaks. Accounting for the tight formations, each horizontal leg had to be stimulated efficiently, despite being inaccessible with coiled-tubing. In addition, well production had to be reliably back-allocated to each drain, and meet pre-defined reservoir guidelines. Despite contrasting properties, all three drains had to be produced at reasonable rates, avoiding that one drain would dominate the other two. And finally, enhanced reservoir understanding was required within each drain, with qualitative indication of their flow profile and associated reservoir conformance.The 3-legged multi-lateral oil producer was drilled and completed successfully. In each of the three horizontal laterals, totaling more than 15,000 feet length, drop-off limited-entry ‘Smart Liners’ were installed, to allow bull-heading stimulation. This offered an effective high-volume matrix acidizing method, adapted to the contrasted properties and tight zones encountered along the laterals. The well was equipped with permanent downhole gauges and inflow control valves (ICV's) to dynamically monitor downhole contributions, modulate production from each drain, avoiding well delivery to be dominated by the highest potential reservoir and control unwanted water/gas production to the surface. To complete the picture, chemical in-flow tracers were installed, in the tubing and within each drain, to monitor the laterals’ flow profiles and performance, and measure the individual contribution from each reservoir. This aimed to determine the efficiency of the ‘Smart Liners’ design and proved a cost-effective option to quantify the contribution from the laterals, compared to running regular PLTs.The resulting pilot is the first well in the world to combine a smart completion with three limited entry ‘smart liners’ utilizing drop-off technique and chemical inflow tracers. The pilot well, which behavior is being evaluated over 2021, provides a groundbreaking approach to evaluate and unlock hydrocarbon resources in a poorly developed area of the field, allowing a significant optimization of well count and of associated capital and operating expenditures.

ADNOC is progressing with large-scale rig-less high-rate matrix stimulation by deployment of advanced lower completions. A key contribution to future production growth is expected to come from these "Smart Liners" that rely on the principles of the limited-entry technique. The concept is based on a number of small, pre-drilled and unevenly spaced holes which enable an even distribution of acid along the entire reservoir drain to be stimulated. The smart liner incorporates swellable packers to avoid annular flow of acid to preferential zones and to isolate segments with heterogeneities. In this work, we focus on aspects related to planning, design and execution of high-rate matrix-acid stimulation of wells. We demonstrate that short wells need a different design approach compared to extended-reach wells and we provide guidelines on how to achieve the highest achievable pump rate and desired acid volume subject to reservoir, well and equipment constraints. The carbonate reservoirs typically exhibit considerable variation in permeability along the well, hence techniques for production profiling, such as inline tracers, are valuable for assessing the actual stimulation effectiveness. Wormhole penetration for a particular acid system will vary depending on the rock petrophysical properties and the reservoir properties; therefore, a systematic data analytics project is on-going.

Reservoir simulation models aim to reproduce at well, sector and field level the pressure and production behavior observed in the historical data. The size and resolution of the models are essentially capped by the computational resources as the numerical computations are quite complex and hardware demanding. For this reason, the use of simulation models to understand inter-field communications at regional level have been always a challenge, rarely pursued, referring those analyses to simple material balance to evaluate influxes, lacking lateral vectors to identify where volumes are coming from, especially on cases of multiple field interactions. The work presented in this paper illustrates the value of merging existing field level simulations models into a large scale regional simulation grids, in order to understand pressure disturbances observed in multiple fields Offshore Abu Dhabi. The process of merging simulation models represents a big challenge considering the high variety of approaches used in the original models, different geology complexity, fluid characteristics, different depletion regimes and field development strategies. In this study, thousands of wells, 6 structures with different fluid and equilibrium regions were used to build the biggest reservoir simulation model in Abu Dhabi. The integration of the data pursues the replication of the existing static and dynamic models, addressing in parallel lateral and vertical upscaling issues when moving from very fine into coarser grids. Implications on the change of scale on the repeatability of the HCIIP volumes and the impact of pseudo relative permeability curves on the history match were carefully analyzed during the process. Evaluation of the impact of the simplifications over the overall quality of the model was of paramount importance, interrogating whether the simplifications affects the capability of the model for assessing the pressure communication and influxes among the fields. The regional simulation model allowed to understand the effects of the peripheral water injection of a giant field on the nearby satellite fields, also the effects of these interactions on the pressure and oil saturation changes through time. Fields and Structures separated way far (20 and 40 Km away) can eventually see pressure disturbances after very long periods of time (up to 300 psi in couple of decades in some cases). Although evidences for changes in pressure are very clear and supported by RFT/MDT time lapsed data, the work also proved that changes on saturations are not very evident or can be considered very marginal on fields separated by large distances. This work represents an alternative and more accurate approach for evaluating nearby field communications and to quantify the boundary conditions to restore models at original stage before nearby interferences, allowing proper initialization of the fine scaled simulation models on pre-production status.

The gas compressibility factor is an important property in reservoir simulation studies. It is directly linked to the gas formation volume factor and the gas density thereby impacting wellhead injection pressure, reservoir voidage, injectivity, as well as the tendency for gas gravity override to occur in the reservoir. ADNOC's PVT database contains experiments on almost 2,000 samples, of which more than 100 have been subject to advanced gas injection experiments. Z-factor data have been compiled from the liberated gas during DV experiments as well as from CCE experiments on reservoir gases, injection gases, and swollen fluid mixtures. Several of these mixtures are very rich in H2S, whereas pressure and temperature are in the range of 14.7-14,500 psia and 80-365 °F, respectively. We test several different methods for predicting the Z-factor, such as the industry-standard Hall-Yarborough method, in combination with various models for pseudo-critical pressure and temperature and including correction for non-hydrocarbon components. Other methods tested include the GERG-2008 model, considered to be state-of-the-art for predicting physical properties for well-described gas mixtures, as well as the well-known Peng-Robinson cubic equation of state. Based on close to 10,000 data points in our database, the GERG-2008 model typically predicts the Z-factor to be within 2% of the measured value, which is on par with the experimental uncertainty. However, for some rich gas condensate mixtures, the model gives larger errors because its parameters are only tuned to compositions with components up to C10. This is to our knowledge the first time that the GERG-2008 EOS has been compared to standard Z-factor correlations for such a large number of data points. If compositional information is available, we recommend using either the GERG-2008 model or the Hall-Yarborough model with pseudo-critical properties provided by Kay (1936). When compositions are not available, we find that the Standing correlation is more accurate than the Sutton model, also for sour mixtures.

Tropical cyclones are some of the most devastating natural hazards and the “three beasts”—Harvey, Irma, and Maria—during the Atlantic hurricane season 2017 are recent examples. The European Centre for Medium-Range Weather Forecasts (ECMWF) is working on fulfilling its 2016–25 strategy in which early warnings for extreme events will be made possible by a high-resolution Earth system ensemble forecasting system. Several verification reports acknowledge deterministic and probabilistic tropical cyclone tracks from ECMWF as world leading. However, producing reliable intensity forecasts is still a difficult task for the ECMWF global forecasting model, especially regarding maximum wind speed. This article will put the ECMWF strategy into a tropical cyclone perspective and highlight some key research activities, using Harvey, Irma, and Maria as examples. We describe the observation usage around tropical cyclones in data assimilation and give examples of their impact. From a model perspective, we show the impact of running at 5-km resolution and also the impact of applying ocean coupling. Finally, we discuss the future challenges to tackle the errors in intensity forecasts for tropical cyclones.

This presentation will give an overview about an ongoing collaboration between the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI). Our recent development is a single-executable coupled configuration of the Integrated Forecasting System (IFS) and the Finite Volume Sea Ice-Ocean Model, FESOM2. This configuration is set up to participate in the DYAMOND project alongside ECMWF’s default IFS-NEMO configuration. IFS-FESOM2 and IFS-NEMO are tentative models to generate “Digital Twin” storm-scale, coupled simulations as envisioned in the European Destination Earth (DestinE) and Next Generation Earth Modelling Systems (NextGEMS) projects.FESOM2 has a novel dynamical core that supports multi-resolution triangular grids. The model and its predecessor FESOM1 have been used in many studies over the last decade, with a focus on the role of the polar regions in global ocean circulation. The impact of eddy-permitting and locally eddy-resolving resolution has been addressed in CMIP6 and HighResMIP simulations as part of the AWI-CM-1-1 global climate model, while simulations with up to 1km resolution in the Arctic Ocean have been performed in stand-alone mode.Initially, two coupled IFS-FESOM2 configurations have been tested: A coarse-resolution setup with a nominal 1° ocean, and a DYAMOND-II configuration with 0.25° ocean and IFS at 4.5km global resolution on average. For the latter configuration, FESOM2 is mimicking the “ORCA025” tri-polar curvilinear grid of the NEMO model, whose grid boxes have been split into triangles. Initialisation is from ECMWF’s analysis for IFS and NEMO, and from an ERA5-forced ocean spin-up for FESOM2. We discuss technical challenges with respect to the hybrid OpenMP and MPI parallelization in a single-executable context, describe a novel strategy for resource-efficient writing of model output, and summarise future applications such as exploring the impact of flexible FESOM2 grid configurations on the atmosphere - with ocean simulations that resolve leads in sea ice and ocean eddies almost everywhere.

Dynamic sea ice and ocean have long been recognised as an important components in the Earth System Models used to generate climate change projections and more recently seasonal forecasts. However, the benefit of forecasts on the timescales of days to weeks has received less attention. Until recently it was assumed that sea-ice-ocean fields change so slowly that it is acceptable to keep them fixed in short and medium-range forecasts. However, at the ice edge the presence of sea ice dramatically influences surface fluxes, particularly when the overlying atmosphere is much colder than the open ocean so errors in the position of the sea ice, caused by simply persisting this field, have the potential to degrade atmospheric skill. To address this and similar issues, the European Centre for Medium-range Weather Forecasts (ECMWF) recently took the pioneering step of coupling a dynamic–thermodynamic sea ice-ocean model to the Integrated Forecast System, developing the first coupled medium-range forecasting system. This was a major step towards making ECMWF’s forecasts seamless across all timescales.In this study we assess the benefits of including coupled sea-ice ocean processes in the medium-range by comparing set of ten-day forecasts with and without dynamic ice-ocean coupling, focussing on forecast performance at the edge of the sea ice and in the surrounding region. We demonstrate that dynamic coupling improves forecasts of the sea ice edge at all leadtimes. Further, the skill gained is larger during periods when the ice edge is advancing or retreating rapidly. We will also explore whether dynamic coupling has an impact on forecast skill in atmospheric parameters downstream of the ice edge.

Earth-System models traditionally use double-precision, 64 bit floating-point numbers to perform arithmetic. According to orthodoxy, we must use such a relatively high level of precision in order to minimise the potential impact of rounding errors on the physical fidelity of the model. However, given the inherently imperfect formulation of our models, and the computational benefits of lower precision arithmetic, we must question this orthodoxy. At ECMWF, a single-precision, 32 bit variant of the atmospheric model IFS has been undergoing rigorous testing in preparation for operations for around 5 years. The single-precision simulations have been found to have effectively the same forecast skill as the double-precision simulations while finishing in 40% less time, thanks to the memory and cache benefits of single-precision numbers. Following these positive results, other modelling groups are now also considering single-precision as a way to accelerate their simulations.In this presentation I will present the rationale behind the move to lower-precision floating-point arithmetic and up-to-date results from the single-precision atmospheric model at ECMWF, which will be operational imminently. I will then provide an update on the development of the single-precision ocean component at ECMWF, based on the NEMO ocean model, including a verification of quarter-degree simulations. I will also present new results from running ECMWF's coupled atmosphere-ocean-sea-ice-wave forecasting system entirely with single-precision. Finally I will discuss the feasibility of even lower levels of precision, like half-precision, which are now becoming available through GPU- and ARM-based systems such as Summit and Fugaku, respectively. The use of reduced-precision floating-point arithmetic will be an essential consideration for developing high-resolution, storm-resolving Earth-System models.

This chapter describes the application of surfactant-based foams for recovery of oil with a focus on subsurface aspects. While the concept of foaming may be qualitatively well understood, the physical behaviour of a foam system comprising gas, brine, and surfactant depends on the type of each of these three constituents and their interaction, in addition to the properties of the porous medium in which the foam is designed to be generated and perhaps propagate. Key physical properties, which must be investigated during a laboratory experimental program, are discussed. A critical review is provided of a number of key applications where foam is utilised for recovery of oil, starting with drilling, completion, and stimulation before moving on to chemical conformance and enhanced oil recovery.

Minimum miscibility pressure (MMP) is an important parameter when designing a miscible gas flood. Traditionally, the MMP is evaluated using slimtube tests, which are time-consuming and expensive. Since it is typically not feasible to test more than a few injection gases on a subset of relevant reservoir fluids, reliable methods are required to estimate the MMP. The most popular correlations are often based on data from low-temperature reservoirs and are not always very reliable.In this work, we make use of the new, comprehensive ADNOC PVT database, which contains more than 100 slimtube MMP measurements for a variety of injection gases, including sour gases. We then complement this data source by performing equation of state (EOS) based miscibility calculations covering a large temperature range with appropriately tuned EOS models. The combination of measured and simulated data is then used as input for development of a general correlation.The new correlation is a modification of the Eakin-Mitch formulation. It has been tuned to a large variety of injection gases and reservoir fluid compositions, and covers a wider temperature range. The average deviation is 5% and the maximum error is less than 20%. Results show that the MMP exhibits a maximum versus temperature, a feature also noticed previously during development of CO2 miscibility (Yuan et al., 2005; Alshuaibi et al., 2019).One of the novelties of this work is the identification of a more complicated temperature-dependency of the MMP. Furthermore, the new correlation considers not just lean gases but also rich gases and sour gases, which develop miscibility based on the combined condensing-vaporizing mechanism. We believe that this model is more robust because it covers a much larger parameter range compared to existing correlations.

Long horizontal or highly deviated wells require lower completion liners to improve well and reservoir management of the carbonate reservoirs in ADNOC's portfolio. Optimum well productivity depends on effective stimulation post drilling. Traditionally, a complicating factor in a matrix-acid stimulation treatment has been poor control of the acid placement along the reservoir section to total depth (TD) without stage equipment or chemical diverters. The limited-entry liner (LEL) lower completion addresses the shortcomings of other stimulation techniques. It consists of a number of unevenly spaced holes with the purpose to distribute acid evenly along the reservoir section to be stimulated. ADNOC is planning to execute a series of pilots to test and possibly expand the operational envelope of the LEL technique.Design of a the hole-size distribution requires a mathematical model, which accounts for the various pressure drops encountered in the system as well as the impact of the chemical reaction between acid and rock. In this work, we present a comprehensive algorithm for designing a hole-size distribution which yields a user-specified acid coverage and also provides the best possible wormhole penetration, subject to a number of constraints related to surface pressure, pump rate, and acid volume available. We make use of findings published over the past 15 years in the SPE literature to arrive at an enhanced design philosophy, which is appropriate for the carbonate reservoirs in ADNOC's portfolio.The design model takes into account a number of important parameters, including fluid and reservoir properties, as well as completion information such as tubing ID, liner ID, and the LEL hole sizes. A check is made using nodal analysis software to ensure that the LEL completion does not act as a flow restriction during back-production.

Miscible gas injection is a proven enhanced oil recovery method for medium-light oils. Miscibility is typically assessed with the slimtube experiment, which aims at identifying the minimum pressure (MMP) above which the displacement process is multi-contact miscible and leads to low microscopic residual oil saturation. During implementation of a miscible gas injection scheme, there will often be areas close to producing wells where the reservoir pressure is below the MMP and the question then arises whether the process can still be considered miscible. Another complicating factor occurs in reservoirs with significant lateral fluid property variation, where it is not intuitively clear how the MMP varies aerially.We employ compositional reservoir simulation to investigate the impact of flowing bottom-hole pressures below MMP on the development and propagation of a miscible front. Starting with 1D simulations, we track a number of important properties such as the effluent gas composition over time, the component K-values at distinct positions along the displacement front, as well as the pressure at the front over time.Due to the high gas mobility, the pressure drop per unit distance is greatly reduced in the areas flooded by gas. This means that the reservoir pressure at the gas-oil front is higher than the pressure at the same position before arrival of the front. Therefore, a miscible displacement can be maintained even when not only the flowing bottom-hole pressure but also the average reservoir pressure is below MMP. A key requirement is, of course, that the gas is injected at a pressure above MMP. We evaluate various parameters in terms of their ability to distinguish between miscible and immiscible displacement and find that the phase density difference versus pressure provides a unique advantage for estimating the MMP and we believe it can be used not only in 1D slimtube simulations but also in other thermodynamics-based algorithms. We also find that the vapor mole fraction, inferred from GOR measurements, is a better metric than GOR itself and that molar ratios applied to the effluent gas are very useful for tracking miscibility.

ADNOC has completed the second phase of its ambitious integrated capacity model (ICM) with the overall aim to optimize its fluid production portfolio from the well level to the processing facilities. The business drivers are to establish capabilities to optimize high-value products and proactively react to market demand changes effectively. Such capabilities required a robust thermodynamics engine with component-wise tracking based on a country-wide capacity model network comprised of a myriad of wells, pipelines, and separators.Fluid samples are not available for all the wells in a field. An innovative workflow was created to assign appropriate composition at the well level based on the data set available for a subset of wells. The captured compositions were then passed to the ICM's hydraulic calculation engine to track the fluid compositions at the required nodes across the network.The existing data model was expanded and user interfaces were created to capture the complexities within the network and visualize the changes in fluid properties, particularly composition, density, and flow rates, at the defined nodes.This digital transformation initiative had to overcome the following complexities to improve accuracy and enable faster decision-making:Incorporation of data from more than 20 fields, 150+ reservoirs, 5000+ wells Optimizing the country-wide network model comprised of wells, pipelines, and separators Performing multiple pre-conceived daily scenarios with 60-month forecasts for production and injection rates Accounting for lateral and vertical composition variation within a reservoir Mixing of fluids at different points in the network at different pressures Implementation of a unified equation of state (EOS) to enable component trackingThe network model successfully captured this complexity and predicted capacities for all custody-transfer points between upstream and downstream networks demonstrating a good match (>90%) between the actual laboratory-based measurements and the ICM results. The tool also offered the capability to maximize production of desired components at the source level to meet the dynamic energy demands of the country, allowing a 1-3% profit improvement in the base operating plans. Alternate scenarios offer additional views on how to obtain the same upstream liquid production targets while maximizing downstream gas revenues, hence overall country profitability. The ICM recommended suitable targets during crisis conditions to react accurately to unexpected market fluctuations.Implementation of the unified EOS along with component tracking creates new avenues for digital transformation by allowing the operator to optimize high value products and answer to demand changes quickly. Multiple scenarios can be analysed and visualised to support decision makers to increase profitability in a highly competitive hydrocarbon market from rock to stock.

A new technique is presented for the development of a common equation of state (EoS) model for multiple petroleum reservoir fluid compositions counting heavy gas condensates, near-critical fluids and black oils. The basic idea is that the phase behavior of a reservoir fluid to a large extent is determined by its critical point. This is not only the case at near-critical conditions. An EoS model matching the critical point of a petroleum reservoir fluid will in general also provide a good match of routine and EOR PVT data for both gas condensates and black oils. A measured critical point will seldom be available, but can be estimated from other available PVT data. The new technique is exemplified through the development of a 9-component common EoS model for 21 H2S rich reservoir fluids, 3 near-critical fluids, and 18 black oils. All 21 fluids had routine PVT data and 11 had solubility swelling data. Using the available PVT data, an approximate critical point was determined for each reservoir fluid composition. The critical point and the saturation point pressure at the reservoir temperature were the only data tuned to when developing the common EoS model. With this simplified method, the time spent on developing a common EoS model for a large number of fluids can be significantly reduced. The method provided a good match of the PVT data for all 21 reservoir fluids including the shift from bubble point to dew point on the swelling curve for the black oil compositions that had solubility swelling data.

Implementing large-scale projects within a company are challenging tasks and often provide a good learning curve that can be beneficial to understand the complexity of the work involved. An integrated subsurface to surface asset modeling solution was implemented at the country level to automate production capacity planning while optimizing shortfall and opportunity identification (Hafez et al., 2018). Several structured business processes support the developed system; it orchestrates the analytical processes followed by the corresponding approval system. A robust data management process was implemented and backed with a business process that includes more than 150 configurable exception rules. Besides, the developed solution leverages the rigor of the first principle and data-driven models to provide a desired and stable outcome ranging from potential evaluation, quota definition, capacity management, business plan validation, and other business processes. The developed solution can isolate wells, sectors, reservoirs, and/or fields for further evaluation. Given the challenge of balancing market demand with profits and subsurface deliverability, a time-efficient, balanced, and integrated solution is expected to provide an edge to an organization in this competitive environment. The Integrated Capacity Model (ICM) system has already been utilized for capacity and deliverability of 2019 and 2020 ADNOC business plans demonstrating 99% agreement with field capacity tests. The system shown +3% profit gains through various production optimization scenarios, while recommending which assets, fields, and/or reservoirs can be targeted to achieve those targets. Developing and implementing the solution at such a large scale surfaced various challenges at organizational, infrastructure, and solutions/workflows. This paper discusses those challenges and the ‘lessons’ learned during the implementation of this solution. Various value-added use cases are presented.

A new approach based on the statistical associating fluid theory is presented here to model eight light crude oils, with the saturate, aromatic, resin, and asphaltene analysis as the only input for the model. Within the characterization procedure of Punnapala and Vargas ( Fuel 2013, 108, 417−429, 10.1016/j.fuel.2012.12.058), the aromaticity parameter and the asphaltene molecular weight were fixed to all crude oil samples, while the asphaltene aromaticity is the only fitted parameter of the model. A correlation for this parameter with the flashed gas molecular weight allows for full predictions of the phase behavior without the need of any asphaltene onset data. The predictive molecular model was used to study asphaltene instability as a function of injected CO2 and natural gas concentration. The model can also accurately reproduce routine pressure–volume–temperature experiments, such as constant composition expansion, differential vaporization, and multi-stage separation tests, performed on the crude oils...

We present an investigation of the performance of the ECMWF coupled atmosphere-waves-ocean model for different ocean and atmosphere resolutions on a series of tropical cyclones in the Western Pacific with the aim to better understand the coupled feedback mechanisms in these exterme conditions. For some of the test cases, we only find little impact of coupling the atmosphere to the ocean, while in others, we observe a very large impact. To further understand these differences, we have selected two tropical cyclones (TCs) as case studies: TC Haiyan (with small impact of coupling) and TC Neoguri (with large impact of coupling). The comparison between these two cases suggests that the upper ocean stratification is the key in determining the strength of the coupled feedback. A strong coupled feedback is found whenever the ocean heat content of the upper layer is low while a very weak coupled feedback is found whenever the ocean has a thick warm mixed layer. The oceanographic response to tropical cyclones for the two storms has been compared to sea surface temperature and derived surface currents from drifting buoys and to subsurface observations from Argo and ship launched XBT's. These comparisons show that we are able to realistically reproduce the atmospheric and oceanographic interaction during tropical cyclone conditions which gives us confidence that the coupled modelling system is physically sound.

In this paper, we investigate the potential for applying miscible nitrogen injection as an enhanced oil recovery (EOR) method for a high-temperature, low-permeability carbonate reservoir, which contains a volatile oil with some H2S and CO2. The field, which is located onshore Abu Dhabi, is still in its early development phases but suffers from relatively low throughput rate because of low permeability. Various gas injection schemes are being considered, with different source gases. At the prevailing reservoir pressures, extensive phase behavior studies confirm that the reservoir fluids develop miscibility with nitrogen, carbon dioxide, and hydrocarbon gas. The simulation studies involve a number of sensitivity runs performed on sector models, which are sufficiently fine-gridded to capture the compositional transition zone propagating between injector and producer pairs. Miscible nitrogen injection comes out as a viable option with the potential to increase recovery by 10 to 20% above the current water flood development scheme. The significantly improved sweep and displacement efficiency are due to N2 miscibility with reservoir oil under reservoir conditions, possibility of increased PV injected in a N2 WAG scheme, and the ability to maintain a higher reservoir pressure (at initial reservoir pressure). From a surface facilities point of view, techniques for N2 capturing is mature, tried and tested. N2 being inert does not pose corrosion risks to well completion and surface facilities. However, capital costs for N2 rejection units – if utilised in a N2 WAG EOR scheme – will need to be taken into account. Although N2 WAG EOR is seen to be very attractive for the reservoir under study, alternative EOR schemes are also being actively evaluated. The aim is to arrive at an optimum EOR project for the reservoir in line the achieving the 70% oil recovery aspiration.

ADNOC has embarked on the second phase of its ambitious integrated capacity model (ICM) project with the overall aim to optimise its fluid production portfolio from the well level to the processing facilities. A key feature of the new software tool is the ability to track and predict fluid properties over time across the entire production network, comprising thousands of wells and a myriad of pipelines.The reservoir fluid composition is assigned at well level for each producing reservoir. The compositional tracking over time is straightforward for many wells, but complicating factors do arise, such asLateral compositional variation related to complex reservoir charging history Vertical compositional gradients, especially for near-critical fluids The presence of initial and secondary gas caps, resulting in gas coning Injection of miscible gas for enhanced oil recoveryThe fluid systems range from medium-API oil to gas condensates and the key chemical components vary as follows: C1 [5-80%], CO2 [0.5-8%], and H2S [0-35%].Mixing of pressurized fluids with different compositions at various junctions in the network requires a robust thermodynamics model to capture the associated variation in fluid properties, particularly density and viscosity as a function of pressure and temperature. We demonstrate that it is possible to constrain one unified equation of state applicable to all fluids, as long as the fluid systems used for the tuning span the entire range of compositions observed. Mixing of fluid streams is computationally much simpler if each stream is made up of the same components (although in different amounts) with the same component properties. On average, the predicted fluid density is within 1% of the measured value from a multi-stage separator test.

High GOR is a constraint for many production wells. Foam has been proved able to reduce GOR in several field production well treatments. The primary goal of this study is to qualify foams for use at reservoir conditions (280 bars, 103 °C). Foam will block gas and thereby reduce Gas-Oil Ratio (GOR) and improve field oil production. Bulk foam experiments and dynamic foam core flooding experiments have been performed to qualify foam for gas shut off at reservoir condition. Foam has been studied in static foam tests using different surfactants, and in limestone core flood experiments at reduced conditions 30 bars and 50 °C. Further foam generation and gas blocking test at reservoir conditions have been conducted both in outcrop and in reservoir material. Dynamic core flooding experiments at 30 barg and 50 °C have shown generation of moderate to strong foams in carbonate limestone core material both with Nitrogen and Methane as the gas phase. Strong foam with mobility reduction factors around 65-70 have been generated in reservoir core material at reservoir conditions. The presence of oil in the carbonate material is a challenge for in-situ foam generation. One solution to the problem has been identified by use of a pre-flush with FS-500 (fluorinated surfactant), prior to alpha olefin sulfonate (AOS). CO2-foamers such as Duomeen has been tested, but gave no foam with Nitrogen as gas phase. Higher concentration of AOS surfactant was able to generate in-situ foam without assistance of fluorinated surfactant. The generated foam is stable also with lower surfactant concentration after first generation. The mobility reduction factor is constant for a range of surfactant concentrations. Strong foam has been generated in carbonate reservoir core material at reservoir conditions. The presence of oil in carbonate material prevents in-situ foam generation at low surfactant concentration, but gives strong foam at higher surfactant concentration.

This paper describes preparations and planning for a campaign of foam gas shut-off pilot operations in a large carbonate reservoir located offshore Abu Dhabi containing an oil column in equilibrium with a large gas cap. Throughout the field history and due to the heterogeneity (permeability ranges from 5 mD to 1 D), the major challenge to produce the oil rim independently from the gas cap was how to control premature gas breakthrough in the oil producers. Mechanical interventions in high gas-oil ratio wells are particularly complicated due to the risk of losing oil potential and are generally unsuccessful.Injection of foam for gas shut-off (FGSO) is a near-wellbore treatment, which has been trialed elsewhere in the industry with some success. Foam can act as an auto-selective agent to shut-off confined gas inflow through a gravity-controlled source like coning or cusping, while oil breaks the foam, resulting in preferential oil flow and reduction in gas-oil ratio. In addition, this type of operation has been identified as an EOR enabler, because it can help prepare for the technical and logistical challenges of using EOR chemicals in the field, generate data useful for the modeling of surfactant and polymer under reservoir conditions, and mitigate early gas breakthrough in the case of gas-based EOR developments.For the reservoir in question, a key complicating factor was to identify a surfactant, which could generate strong foam in-situ (mobility reduction factor of 50) at harsh reservoir conditions (temperature of 220-230 °F and water salinity above 200,000 ppm, including 20,000 ppm divalents), with an acceptable level of adsorption. The candidate selection process took into consideration overall behavior of the reservoir as well as performance of the individual high-GOR wells. Target well selection criteria included homogeneity of permeability, an understanding of gas sources and their movement, and observation of a rate- or draw-down-dependent GOR.The experimental lab program involved testing several surfactant formulations in bulk as well as in corefloods with and without the presence of reservoir oil to evaluate foaming ability and level of gas flow reduction. One formulation showed the right level of in-situ mobility reduction, in addition to stability and moderate adsorption at the prevailing reservoir conditions, and was therefore selected for a pilot test involving four wells.

Minimum miscibility pressure (MMP) is an important parameter in the design of a successful miscible gas injection project. It is common industry-practice to infer MMP from a series of carefully designed slimtube tests, but there are cases where a quick and rough estimate of MMP is required to screen for potential application of miscible gas injection.Mathematical correlations are attractive because they require few input parameters, little information about the fluids, and are quick and easy to use. Whereas MMP using hydrocarbon gas or nitrogen can be predicted with relative confidence using standard correlations, the same level of accuracy is not found for correlations predicting CO2 MMP, in part because of a more complicated temperature-dependent mass transfer.This study is part of an ongoing effort to evaluate miscibility conditions using a variety of injection gases across the ADNOC reservoir fluid portfolio. We have tested the most common existing CO2 MMP correlations against measured MMP data from slimtube tests for a large number of Abu Dhabi reservoir fluids. The results show that none of the existing correlations can accurately predict MMP for these crude oils and we believe that this is attributed to the fact that most available correlations were established based on reservoir temperatures much lower than the typical temperature conditions of 250 °F found in many Abu Dhabi reservoirs.We therefore propose a new correlation for estimating CO2 MMP with particular emphasis on high-temperature fluid systems. The new formulation has been developed based on multiple-parameter regression of measured data as a function of temperature, saturation pressure and reservoir fluids composition. In addition to the existing data sets available in the literature, we performed 17 additional slimtube experiments on different Abu Dhabi reservoir fluids to enlarge the parameter envelope and increase robustness of the model. The proposed new correlation predicts CO2 MMP within 6.4%. To constrain the correlation, we performed a series of EOS-based MMP calculations for several fluids in the temperature range of 100 to 350 °F. These calculations confirm that MMP attains a maximum value around 320 °F.

Many of the oil reservoirs in ADNOC’s portfolio have been producing for decades and continue to deliver their target rates, thanks to development schemes centred on pressure support from peripheral water injection and/or crestal gas injection, where applicable, or from line-drive patterns.As these reservoirs mature, a number of enhanced oil recovery (EOR) techniques are being evaluated to increase the ultimate recovery factor. Choosing one or several appropriate EOR methods starts with a robust screening methodology, which in this case has to apply across an entire reservoir portfolio, rather than just to a single asset.The key objective of the screening efforts presented in this paper is to estimate the EOR potential from each reservoir in a systematic manner to allocate the right resources, to the right fields, at the right time, with the right technology. Therefore, the screening methodology must take into consideration the following aspects: The focus of the screening is on identifying opportunities, which are or will become technically feasible by the time deployment is requiredThe screening procedure must remain sufficiently high-level to be able to deliver an outcome within a short time-frameA single EOR method is not necessarily applicable across an entire reservoir; hence several EOR methods can potentially be implemented in the same reservoir but in different areasThe thought process (workflow) has to be properly documented so that the screening exercise can be updated whenever new technologies or new relevant field data become availableThe new EOR screening tool considers more EOR methods compared to previous screening efforts: It takes a much broader view on potential source gas options for miscible gas injection, namely enrichment of sweet hydrocarbon gas, sour gas streams, CO2 with or without impurities, as well as nitrogen for some high-temperature reservoirs containing volatile oil. This step involved estimating miscibility conditions for hundreds of combinations of injection gas compositions and reservoir fluids using equation-of-state based MMP calculations in addition to common MMP correlations.The reservoirs are all carbonate formations and are characterized by high temperature and high salinity. These conditions have traditionally been a challenge for chemical EOR methods involving polymers and surfactants. However, recent R&D progress has opened up for opportunities not previously considered. Both polymer and foam agents are currently being piloted, which have the potential to change the EOR landscape in Abu Dhabi and perhaps elsewhere as well.As reservoirs mature and breakthrough of injection fluids begin to occur, the need for improved reservoir conformance becomes evident. The success of any EOR technique relies on the right well placement with the right monitoring in place and the right level of injection profile control.

Meeting energy demands and generating profit to shareholders is a continuous quest for oil and gas companies. Production and business planning in integrated oil and gas operating companies is a complex process involving numerous organizations, historic data collection, modeling, prediction, and forecasting. Integrated business planning complexity intensifies due to the uncertain nature of past facts and future conditions. We propose a framework for integrating upstream and downstream production planning processes using data-driven models representing the upstream capacities, downstream processes, and a countrywide profit model. The upstream production model forecasts optimum capacity scenarios of the reservoir fluids with their compositional characteristics and hydraulic performance of the surface facilities while honoring business rules, and based on the various long-term expenditure scenarios, downtime requirements, and downstream demand schedules. An integrated optimization model for value chain has the potential to protect profitability for oil and gas companies in times of unbalanced market forces.

Gas injection is a proven EOR method in the oil industry with many well-documented successful field applications spanning a period of more than five decades. The injected gas composition varies between projects, but is typically hydrocarbon gas, sometimes enriched with intermediate components to ensure miscibility, or carbon dioxide in regions such as the Permian Basin, where supply is available at an attractive price.Miscible nitrogen injection into oil reservoirs, on the other hand, is a relatively uncommon EOR technique because nitrogen often requires a prohibitively high pressure to reach miscibility. Unlike other injection gases, the minimum miscibility pressure for nitrogen decreases with increasing temperature. In fact, in deep, hot reservoirs containing volatile oil, nitrogen may develop miscibility at a pressure similar to the MMP for hydrocarbon gas or carbon dioxide. The phase behavior is more complicated than what can be captured by correlations and hence requires equation-of-state calculations.Results from a recent EOR screening study in ADNOC indicate that a couple of high-temperature oil reservoirs in Abu Dhabi may be potential targets for miscible nitrogen injection. This paper discusses key aspects of the EOS modeling. Advanced gas injection PVT data are available to enable a fair comparison between nitrogen, carbon dioxide and lean hydrocarbon gas. In this work, we have modelled and analyzed the phase behavior of two volatile oil systems with respect to nitrogen, hydrocarbon gas, and carbon dioxide injection, as part of a reservoir simulation study, which will be covered in a subsequent publication; see Mogensen and Xu (2019). Nitrogen behaves differently from hydrogen carbon gas, despite the fact that the two gases lead to similar minimum miscibility pressures. At the prevailing reservoir pressure, the swelling factor with hydrocarbon gas is four times higher than for nitrogen. Furthermore, the reservoir fluid density increases during swelling with nitrogen, whereas it decreases as a result of hydrocarbon gas swelling. The same trend is observed for viscosity. Injection gas blends with various proportions of nitrogen and carbon injection shows that the MMP is constant when more than 35-40% nitrogen is present in the blend.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.