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Accurate estimation of permeability is critical for oil and gas reservoir development and management, as it controls production rate. After assessing numerical techniques ranging from pore network modeling (PNM) to the lattice Boltzmann method (LBM), an AI-based workflow is developed for a quick and accurate estimation of the permeability of a complex carbonate rock from its X-ray micro-computed tomography (micro-CT) image. Following features engineering using both image processing and PNM, we trained and tested the workflow on thousands of segmented 3D micro-CT images using both shallow and deep learning algorithms to assess the permeability. A broad variety of supervised learning algorithms are implemented and tested, including linear regression, support vector regression, improved gradient boosting, and convolutional neural networks. Additionally, we explored a hybrid physics-driven neural network that takes into account both the X-ray micro-CT images and petrophysical properties. Finally, we found that the predicted permeability of a complex carbonate by machine learning (ML) agrees very well with that of a more computationally-intensive voxel-based direct simulation. In addition, the ML model developed here provides a substantial reduction in computation time by roughly three orders of magnitude compared to that of the LBM. This paper highlights the crucial role played by features engineering in predicting petrophysical properties by machine and deep learning. The proposed framework, integrating diverse learning algorithms, rock imaging, and modeling, has the potential to quickly and accurately estimate petrophysical properties to aid in reservoir simulation and characterization.

Seepage-type gas hydrate accumulation in subsea shallow formations involves complicated thermo-hydro-solid coupling processes and matching problem between various accumulation elements. The formation physical properties control local natural gas migration pathway and thus the final reservoir characteristics of hydrates. In this paper, a novel mixed-flux model for gas hydrate accumulation is established and then used to simulate the process of methane gas migration into the shallow stratum to form a hydrate reservoir. The effects of reservoir heterogeneity and gas source conditions on the distribution of pore fluid and hydrate accumulation are examined. The simulation results show that reservoir heterogeneity is conducive to the retention and lateral migration of CH4 in a hydrate stability zone. CH4 can contact more pore water to form a large hydrate reserve, but the formed hydrate is often dispersed. Low-permeability layers enhance the trapping of CH4 and form a uniform and large hydrate saturation. Besides, gas source conditions have an important impact on the hydrate accumulation in reservoirs. Large gas flux, small pore water flux, continuous gas supply, high content of heavy components in natural gas, and numerous gas source points contribute to large amounts of hydrates generation in a certain time period. The presented work will deepen our understanding of the controls of natural gas hydrate systems in subea shallow formations.

This study extends an integrated field characterization in Eagle Ford by optimizing the numerical reservoir simulation of highly representative complex fractured systems through embedded discrete fracture modeling (EDFM). The bottom-hole flowing pressure was history-matched and the field production was forecasted after screening complex fracture scenarios with more than 100 000 fracture planes based on their propped-type. This work provided a greater understanding of the impact of complex-fractures proppant efficiency on the production. After compaction tables were included for each propped-type fracture group, the estimated pressure depletion showed that the effective drainage area can be smaller than the complex fracture network if modeled and screened by the EDFM method rather than unstructured gridding technique. The essential novel value of this work is the capability to couple EDFM with third-party fracture propagation simulation automatically, considering proppant intensity variation along the complex fractured systems. Thus, this work is pioneer to model complex fracture propagation and well interference accurately from fracture diagnostics and pseudo 3D fracture propagation outcomes for multiple full wellbores to capture well completion effectiveness after myriads of sharper field simulation cases with EDFM.

The oil and gas fields are commonly developed with a group of production wells. Therefore, it can be essential for the industries to predict the performance of the production wells in order to optimize the development strategies. In practice, it frequently happens that we only hope to study the performance of a single production well. In such cases, it can be time consuming to run the reservoir simulation with the entire reservoir model to study the well performance. Hence, it can be preferred to determine the control volume (or drainage volume) of the target well from the entire reservoir and run the simulation with the small control volume to reduce the simulation cost. However, an irregular layout of the production wells and the heterogeneity of reservoir properties, which can be commonly observed in real field cases, can induce a stringent barrier for one to determine the control volumes. At present, we are still lacking a method to determine the control volumes of the production wells considering well distribution and reservoir heterogeneities. In order to overcome such a barrier, the authors proposed a new approach to divide the entire reservoir into small control volumes on the basis of the fast marching method (FMM). This approach is validated by comparing the simulation outputs of the target well calculated only with the determined control volume to those calculated with the entire reservoir model. The calculated results show that using the control volume that is determined with the proposed method to calculate the well performance can yield results that agree well with the results that are calculated with the entire reservoir model. This indicates that this proposed method is reliable to determine the control volume of the production wells. In addition, the calculated results in this work show that changing fracture length exerts a slight influence on the control volumes if the length of all fractures is increased, whereas, if only one of the fracture lengths is increased, the control volume of the corresponding well will be significantly increased. The number of the production wells and the distribution of the production well can noticeably influence the control volumes of the production wells. The findings of this study can help for optimizing the well spacing, estimating the ultimate recovery, and reducing the computational cost.

After a cyclic steam stimulation (CSS) process achieved accidental success in recovering heavy oil resources in the 1960s, thermal recovery processes started to unlock the door of in situ recovery ...

The effect of poroelastic properties of the shale matrix on gas storage and transport mechanisms has gained significant attention, especially during history-matching and hydrocarbon production forecasting in unconventional reservoirs. The common oil and gas industry practice in unconventional reservoir simulation is the extension of conventional reservoir simulation that ignores the dynamic behavior of matrix porosity and permeability as a function of reservoir effective net stress. This approach ignores the significant impact of the poroelastic characteristics of the shale matrix on hydrocarbon production. The poroelastic characteristics of the shale matrix highly relate to the shale matrix geomechanical properties, such as the Young’s Modulus, Poisson’s ratio, bulk modulus, sorption behavior, total organic content (TOC), mineralogy and presence of natural fractures in the multi-scale shale structure. In this study, in order to quantify the effect of the poroelasticity of the shale matrix on gas production, a multi-continuum approach was employed in which the shale matrix was divided into organic materials, inorganic materials and natural fractures. The governing equations for gas transport and storage in shale were developed from the basic fundamentals of mass and momentum conservation equations. In this case, gas transport in organics was assumed to be diffusive, while gas transport in inorganics was governed by convection. Finally, a fracture system was added to the multi-scale shale gas matrix, and the poroelastic effect of the shale matrix on transport and storage was investigated. A modified Palmer and Mansoori model (1998) was used to include the pore compression, matrix swelling/shrinkage and desorption-induced deformation of shale organic matter on the overall pore compressibility of the shale matrix. For the inorganic part of the matrix, relations between rock mechanical properties and the pore compressibility were obtained. A dual Langmuir–Henry isotherm was also used to describe the sorption behavior of shale organic materials. The coupled governing equations of gas storage and transport in the shale matrix were then solved using the implicit finite difference approach using MATLAB. For this purpose, rock and fluid properties were obtained using actual well logging and core analysis of the Marcellus gas well. The results showed the importance of the poroelastic effect on the pressure response and rate of gas recovery from the shale matrix. The effect was found to be mainly due to desorption-induced matrix deformation at an early stage. Coupling the shale matrix gas production including the poroelastic effect in history-matching the gas production from unconventional reservoirs will significantly improve engineering completion design optimization of the unconventional reservoirs by providing more accurate and robust production forecasts for each hydraulic fracture stage.

An appropriate well spacing plan is critical for the economic development of shale gas reservoirs. The biggest challenge for well spacing optimization is interpreting the subsurface uncertainties associated with hydraulic and natural fractures. Another challenge is the existence of complex natural fractures. This work applied an integrated well spacing optimization workflow in shale gas reservoirs of the Sichuan Basin in China with both hydraulic and natural fractures. The workflow consists of five components: data preparation, reservoir simulation, estimated ultimate recovery (EUR) analysis, economic calculation, and well spacing optimization. Firstly, the multiple realizations of thirteen uncertain parameters of matrix and fractures, including matrix permeability and porosity, three relative permeability parameters, hydraulic fracture height, half-length, width, conductivity, water saturation, and natural fracture number, length, and conductivity, were captured by the assisted history matching (AHM). The fractures were modeled by the embedded discrete fracture model (EDFM) accurately and efficiently. Then, 84 AHM solutions combining with five well spacing scenarios from 517 ft to 1550 ft would generate 420 simulation cases. After reservoir simulation of these 420 cases, we forecasted the long-term gas production for each well spacing scenario. Gas EUR degradation and well interference would imply the critical well spacing. The net present value (NPV) for all scenarios would be calculated and trained by K -nearest neighbors (KNN) proxy to better understand the relationship between well spacing and NPV. In this study, the optimum well spacing was determined as 793 ft, corresponding with a maximum NPV of 18 million USD, with the contribution of hydraulic fractures and natural fractures.

Physics-based reservoir simulation is the backbone of many decision-making processes in the oil and gas industry. However, due to being computationally demanding, simulating a model multiple times in iterative studies, such as history matching and production optimisation, is extremely time intensive. This downside results in it being practically impossible to update the model every time a set of new data are available. One of the popular solutions for this problem is creating a proxy model of the desired reservoir. However, the consequence of this approach is that such a proxy model can only represent one corresponding reservoir, and, for every new reservoir, a new proxy model must be rebuilt. Additionally, when the overall runtime is considered, it can be observed that, in some cases, iteratively running a numerical reservoir simulation may be quicker than the process of building, validating and using a proxy model. To overcome this obstacle, in this study, we used deep learning to create a data-driven simulator, deep net simulator (DNS), that enables us to simulate a wide range of reservoirs. Unlike the conventional proxy approach, we collected the training data from multiple reservoirs with completely different configurations and settings. We compared the precision and reliability of DNS with a commercial simulator for 600 generated case studies, consisting of 500,000,000 data points. DNS successfully predicts 45%, 70% and 90% of the cases with a mean absolute percentage error of less than 5%, 10% and 15%, respectively. Due to the indirect dependency of DNS on the initial and boundary conditions, DNS acts incredibly fast when compared with physics-based simulators. Our results showed that DNS is, on average, 9.25E+7 times faster than a commercial simulator.

Constructing a reservoir simulation model requires an accurate definition of saturation functions, including capillary pressure and relative permeability data. Usually, these saturation functions are available only for a limited number of samples. Assigning saturation functions to the simulation model requires finding a clear relationship between them and a 3D predictable property of grid cells. Different indices are usually evaluated to describe such a relationship. It was recently shown that each petrophysical dynamic rock type should form similar True Effective Mobility (TEM) curves. In this study, TEM-function was used to evaluate the reliability and performance of different rock typing indices in defining dynamic rock types. FZI, FZIM, MFZI, and FZI∗ (FZI-star) indices were considered and tested on a collection of laboratory-derived core-flooding data from an Iranian carbonate reservoir. Results showed that FZI-star had a better performance than FZI, MFZI, and FZIM.

Interfacial tension (IFT) is important in developing oilfield exploration and production strategies, enhanced oil recovery, and oil-water separation. Tests using reservoir simulators such as Eclipse show that the selected IFT has significant impact on prediction of water coning and production. In petroleum engineering, IFT is often assessed through correlations based on bulk properties excluding the impact of surface-active components. Crude oil contains many different types of surface-active components among which naphthenic acids and asphaltene-like molecules play an important role in IFT between oil and water. It is desirable to be able to link the IFT to the concentration of the natural surfactants to predict variations across a petroleum reservoir. To develop this relation, the equilibrium IFT was investigated in various systems of varying acid content. As expected, naphthenic acids reduce IFT of oil and water significantly. The IFT reduction is however dependent on the structure and composition of naphthenic acids as well as the nature of the solvent. To include the natural surfactancy in the model a Gibbs-Langmuir approach is used to describe the impact of naphthenic acids content in crude oil on IFT. This is compared to two models currently used to estimate IFT in the petroleum industry. The current models are not equipped to capture the role of natural surfactants and hence results may lead to erroneous predictions on crude oil production. We therefore propose an approach where bulk properties that estimate the bulk IFT is used in combination of a term based on the Gibbs-Langmuir model to capture the perturbation by the naphthenic acids to yield more accurate estimates of IFT. This require fitting to at least two experimental points to yield accurate results.

A forecasting method of oil well production based on multivariate time series (MTS) and vector autoregressive (VAR) machine learning model for waterflooding reservoir is proposed, and an example application is carried out. This method first uses MTS analysis to optimize injection and production data on the basis of well pattern analysis. The oil production of different production wells and water injection of injection wells in the well group are regarded as mutually related time series. Then a VAR model is established to mine the linear relationship from MTS data and forecast the oil well production by model fitting. The analysis of history production data of waterflooding reservoirs shows that, compared with history matching results of numerical reservoir simulation, the production forecasting results from the machine learning model are more accurate, and uncertainty analysis can improve the safety of forecasting results. Furthermore, impulse response analysis can evaluate the oil production contribution of the injection well, which can provide theoretical guidance for adjustment of waterflooding development plan.

This paper presents a novel petrophysical experiment for measuring the 3D permeability tensor at reservoir conditions in fractured vuggy carbonate rock. The permeability variation with confining pressure is demonstrated through the experimental investigation; the permeability ellipse plots show the decrease of permeability, as a function of the increase in effective stress. The experiment was developed at reservoir conditions (120 °C temperature and variable confining pressure) in a native whole rock sample, from the Gulf of Mexico naturally fractured and vuggy reservoir. The analysis of the laboratory data reveals the elliptical behavior of permeability. Two ellipses were generated, one corresponding to the fracture network (secondary porosity system) and another for matrix porosity and permeability (primary porosity system). An important result is the compaction of the representative fracture network ellipse until it fuses with the matrix porosity ellipse at a high confining pressure state. The test contributes to the understanding of the mechanical and flow aspects of permeability, for naturally fractured reservoirs. The experimental results provide a basis for improving reservoir simulation studies, to obtain realistic predictions of reservoir behavior.

Summary In this paper, we aim to enhance our understanding of the multiphysical processes in carbon dioxide (CO2)-enhanced-oil-recovery (EOR) (CO2-EOR) operations using a modeling approach. We present the development of a comprehensive mathematical model for thermal/hydraulic/mechanical (THM) simulation of CO2-EOR processes. We adopt the integrated-finite-difference method to simulate coupled THM processes during CO2-EOR in conventional and unconventional reservoirs. In our method, the governing equations of the multiphysical THM processes are solved fully coupled on the same unstructured grid. To rigorously simulate the phase behavior of a three-phase, nonisothermal system, a three-phase flash-calculation module, dependent on the minimization of Gibbs energy, is implemented in the simulator. The simulator is thus applicable to both miscible and immiscible flooding simulations under isothermal and nonisothermal conditions. We have investigated the effect of cold-CO2 injection on injectivity as well as on phase behavior. We conclude that cold-CO2 injection is an effective way to increase injectivity in tight oil reservoirs and reduces overriding effect in high-water-bearing reservoirs. Using the developed general simulation framework, we have discovered and studied several intriguing multiphysical phenomena that cannot be captured by commonly used reservoir simulators, including the temperature-decreasing phenomenon near the production well and the permeability-enhancement effect induced by the thermal unloading process. These phenomena can be captured only by the fully coupled multiphysical model. The novelty of this paper lies in its integration of multiple physical simulation modules to form a general simulation framework to capture realistic flow and transport processes during CO2 flooding, and in revealing the behavior of cold-CO2 injection under THM effects.

The oil density at the bubble point is an important thermodynamic property required in reservoir simulation and production engineering. A higher-accuracy estimate of this property would improve the accuracy of reservoir and production engineering calculations. The bubble point oil density is obtained either from separator tests of reservoir fluids or from differential gas liberation tests. A new procedure utilizing separator and differential tests is proposed whereby the experimental data yield a unique value with high accuracy for the bubble point oil density. A consistent correction of other PVT properties, which are influenced by the bubble point oil density, is required to reflect the unique density value. A quantitative quality control index is defined to measure the quality of PVT laboratory reports. This is achieved by utilizing the unique property of the bubble point oil density, which is usually ignored.

Volume rendering techniques are essential to inspect volume data. In this work, we consider volume rendering for unstructured meshes. We focus on the visualization of black oil reservoir simulation models, represented by irregular hexahedral meshes with geometry distortions and discontinuities. We conduct an experimental study comparing three different strategies to compute the volume integration. The first strategy resamples the model in a regular grid; the second linearizes the model geometry and the variation of the scalar field within a hexahedron cell; the third uses an accurate evaluation of the scalar field at each sample. We compare performance and image quality delivered by each strategy by running a set of experiments with different models. We also investigate the gain in perception applying directional ambient occlusion shading effects. All strategies were entirely implemented on the graphics card.

As oil and gas extraction activities move into deeper rock formations, many experimental studies and field investigations indicate rock exhibits a plastic behavior rather than a pure linear elastic behavior, so poroelastoplasticity must be taken into account in the reservoir simulation. Because reservoir rock is a porous material consisting of a compressible solid matrix and number of compressible fluids occupying the pore space, fully coupled modeling is required for reservoir simulation considering solid-fluid interaction, complex stress conditions and nonlinear behaviors. But the computational process could be cumbersome when constant tangent stiffness method is used to address the poroelastoplastic behavior. In this paper, a fully coupled poroelastoplasticity reservoir model based on Drucker-Prager yield criterion is implemented the tangent stiffness method, and the computational efficiency is compared with the constant stiffness method. The accuracy of these two methods is demonstrated in one-dimensional consolidation. In a case study, these two methods are used to analyze the stresses and pore pressure of a reservoir and computing results and running efficiency are compared. Also, the linear elastic and nonlinear solutions are compared in one-dimensional consolidation and reservoir modeling. It shows that the difference between results by constant stiffness method and tangent stiffness method is very small, while the tangent stiffness method shows significantly fewer iteration numbers and shorter running time than the constant stiffness method.

SummaryIn coal-seam-gas (CSG) fields, where single wells tap multiple seams, it is likely that some of the individual seams hardly contribute to gas recovery. This study aims to examine the contribution of individual seams to the total gas and water production considering that each seam can have different properties and dimensions. A sensitivity analysis using reservoir simulation investigates the effects of individual seam properties on production profiles.A radial model simulates the production of a single CSG well consisting of a stack of two seams with a range of properties for permeability, thickness, seam extent, initial reservoir pressure, coal compressibility and porosity. The stress dependency of permeability obeys the Palmer and Mansoori (1998) model. A time coefficient (α) relates seam radius, viscosity, porosity, fracture compressibility, and permeability. It is used to aid interpretation of the sensitivity study. Finally, two hypothetical simulation scenarios with five seams of different thicknesses and depths obtained from producing wells are explored. The range in properties represents conditions found in the Walloon Coal Measures (WCM) of the Surat Basin, relevant to the Australian CSG industry.Each seam in the stack achieves its peak production rate at different times, and this can be estimated using α. Seams with lower α reach the peak gas rate earlier than those with higher α-coefficient. The distinct behavior of gas-production profiles depends on the combination of individual seam properties and multiseam interaction. At a αratio > 1 (i.e., αtop/αbottom > 1), the bottom seam peaks first but achieves lower gas recovery than the top seam. An increasing αratio is associated with the inhibition of less-permeable seams and reduced overall well productivity. For αratio

Reservoir simulator is widely known in the petroleum industry for analysing and predicting the fluid flow behaviour through porous media. Conventional mathematical approach, which is the mostly used approach in reservoir simulation, assumes several unrealistic assumptions. Introducing the memory formalism with classical mathematical approach can eliminate this shortcoming. Because, the assumptions of fundamental laws (e.g., mass conservation, equation of state, and constitutive equation) used in reservoir simulation are reduced through incorporation of memory formalism. Memory-based flow model accounts the time varying nature of rock-fluid properties. To date, flow models do not yet exist where all the possible reservoir properties are considered as a function of time and space. The present study shows a comprehensive workflow in developing a memory-based reservoir simulator. The fundamentals of memory formalism and fractional calculus along with the use of fractional order derivative to formulate memory formalism are summarized. The proposed memory approach is validated using an application. A comparison of memory approach and multi-continuum approach is shown to depict the significance of the memory formulation for anomalous diffusion. This research is expected to open a door for introducing non-linear solvers in reservoir simulation study where cloud points solutions will be obtained instead of a single point solution. The outcome of this study will help engineers and researchers to develop more transparent simulator instead of creating a black box.

We present a model framework for dynamic simulation of the flow of (Formula presented.) and other components in an injection well coupled with a near-well reservoir. With a partitioned approach, we employ numerical methods adapted to the governing equations in each domain. In both well and reservoir, accurate thermodynamics are used. This model can be used for various design and operational considerations for (Formula presented.) -injection wells, that is, the quantification of pressure and temperature transients, and phase composition, including the appearance of a water-rich phase. We study cases where the flow in the well and the near-well reservoir is dynamically coupled. The rock properties are seen to strongly affect the well dynamics, both regarding magnitude and transient evolution of the bottom-hole pressure. We consider a variation of this case where water is co-injected with (Formula presented.), showing that this is a potential method to mitigate salt precipitation. We also consider intermittent injection, representing the case of direct injection from ships transporting (Formula presented.). Finally, we observe that in the present cases, a coupled model (as opposed to a well-only model) is necessary in order to capture the dynamics in the well during injection. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

Natural gas from ultra-deep reservoirs has been a major contributor for reserves boost, deliverability construction, and profits growth in natural gas industry in China. As a significant strategic domain in the future development of upstream business, the high-quality development of ultra-deep gas resources has great significance for economic benefits enhancement and sustained regional supply assurance. In this paper, based on the appraisal of development characteristics and effectiveness in the developed large ultra-deep gas fields, challenges for high quality development were indicated, which include the difficulties in structure confirm, uncertainties in reserves define and production optimization, risks of aquifer water early breakthrough and high investment of deep wells. Through indoor physical simulation experiments, reservoir characterization, performance evaluation, reservoir simulation and knowledge acquisition from analogous fields at home and abroad, the connotation and requirements for high quality development were discussed, and furthermore, strategies and proposals were thus proposed as follows: to strengthen the pre-development reservoir evaluation to define movable gas reserves and quantify rational production rate so as to avoid facility waste; to optimize both well pattern and well flow rate to achieve uniform depletion and high EUR; to continuously enhance drilling & completion technologies to further reduce drilling and completion circle and cost and targeted reservoir stimulation technologies to enhance movable reserves and single-well productivity and increase the depletion of inferior reserves; and to innovate management modes to establish scientific programs and procedures for the construction, production and operation of ultra-deep gas fields and strictly control the upper limit index of production rate so as to emphasize quality benefits. In conclusion, high-quality development of ultra-deep gas fields, a hard and complex system though, will be possibly achieved only by continuous innovation of exploration and development technologies and management modes.

We explore the petrophysical behavior of the two interbedded lithofacies (sandy silt and clayey silt) that constitute the Green Canyon Block 955 hydrate reservoir in the deep-water Gulf of Mexico by performing experiments on reconstituted samples of the reservoir material. Sandy silts reconstituted to the in situ porosity have a permeability of 11.8 md (1.18 × 10−14 m2), which is similar to the intrinsic permeabilities measured in intact cores from hydrate reservoirs of similar grain size offshore Japan (Nankai Trough) and offshore India. Reconstituted clayey silts have a much lower intrinsic permeability of 3.84 × 10−4 md (3.84 × 10−19 m2) at the in situ stress. The reconstituted sandy silt is less compressible than the clayey silt. Mercury injection capillary pressure measurements demonstrate that the largest pores with the clayey silt are still smaller than the pores remaining after 90% hydrate saturation in sandy silt. We interpret that the methane solubility in pores of clayey silt is always less than that necessary to form hydrate, which explains why no hydrate is present in the clayey silt. We upscale the reservoir properties to estimate the behavior of interbedded sandy silt and clayey silt. We find the upscaled intrinsic horizontal and vertical permeabilities for the entire reservoir interval are 8.6 md (8.6 × 10−15 m2) and 1.4 × 10−3 md (1.4 × 10−18 m2). We estimate that during reservoir production, a maximum vertical strain of approximately 12% will result. Ultimately, this study will inform reservoir simulation models with petrophysical properties at scales of both individual lithofacies and reservoir formation.

SummaryUsing fast and reliable proxies instead of sophisticated and time-consuming reservoir simulators is of great importance in reservoir management. The capacitance-resistance model (CRM) as a fast proxy has been widely used in this area. However, the inadequacy of this proxy for simplifying complex reservoirs with a limited number of parameters has not been addressed appropriately in related works in the literature. In this study, potential uncertainties in the modeling of the waterflooding process in the reservoir by the producer-based version of CRM (CRMP) are formulated, leading to embedding a new error-related term into the original formulation of the proxy. Considering a general form of the model error to represent both white and colored noises, a system of a CRMP-error equation is introduced analytically to deal with any type of intrinsic model imperfection. Two approaches are developed for the problem solution including the following: tuning the additional error-related parameters as a complementary stage of a classical history-matching procedure, and updating these parameters simultaneously with the original model parameters in a data-assimilation approach over model training time. To validate the model and show the effectiveness of both solution schemes, the injection and production data of a water-injection procedure in a three-layered reservoir model are used. Results show that the error-related parameters can be matched successfully along with the model original variables either in a routine model calibration procedure or in a data-assimilation approach by using the ensemble-based Kalman filter (EnKF) method. Comparing the average of the obtained range for the liquid rate as the problem output with true data demonstrates the effectiveness of considering model error. This leads to substantial improvement of the results compared with the case of applying the original model without considering the error term.

Accumulation of liquids in gas-condensate reservoirs may lead to a vertical flow of liquid caused by density difference in the presence of gravity. In this paper, the effect of this vertical flow on the prediction of production data has been investigated. To achieve this goal, a single-layer synthetic cylindrical reservoir is considered and, the flows are simulated with and without vertical direction grid blocks with two lean and rich fluids. The governing equations of the so-called Modified Black-Oil (MBO) model are solved in a cylinder sector using the open-source Matlab Reservoir Simulation Toolbox (MRST). When the vertical flow is taken into consideration, the results show that gravity may lead to migration of liquid condensates to lower regions and the production rates decrease as a consequence of wellbore partially blockage especially for the rich fluid. In addition, gravity may cause a non-linear effect on the flow behavior and increase in the condensates production rates. All results are also obtained for three values of critical liquid condensate saturation to show the effect of this parameter on the production. It has been shown that neglecting the vertical flow may cause significant errors in the result of production, i.e. up to 25% when the fluid is rich and the critical condensate saturation is 0.15. To generalize the effect of the reservoir thickness, dimensional analysis is carried out which yields a similarity parameter. The findings of this study can help for a better understanding of near well flow in gas-condensate reservoirs. Based on this study, in applications in which the reservoir gas is rich in condensate and the thickness of the reservoir layer or the vertical permeability is high enough, it is recommended that the gravity effect is taken into account by enough grid points in the vertical direction in each layer.

Petrophysical rock typing plays a major role in carbonate reservoir characterization and geological modeling for reservoir simulation. In this study, based on 1120 samples of mercury-injection capi...

Reservoir simulation using high-fidelity fluid models is typically employed to study subsurface displacement processes that involve complex physics. Such processes are highly nonlinear and occur at the interplay of phase thermodynamics (phase stability and split) and rock/fluid interaction (relative-permeability). Due to its nonlinearity, relative-permeability is an important constitutive of the conservation equations, and has a significant impact on the simulation. Dependence of the phase relative-permeability on fluid compositions, pressure, and temperature is well-documented. In compositional reservoir simulation, however, relative-permeability is typically modeled as a function of phase saturation. Such an approach may lead to serious discontinuities in relative-permeability. To alleviate this issue, several authors proposed models based on phase state indicators (density, parachor, Gibbs Free Energy). However, such techniques cannot represent the complete degrees of freedom that are exhibited by compositional displacements. In this work, we present a relative-permeability model based on a parameterization of the compositional space. The model is independent of the hydrocarbon phase labeling as gas or oil. We show that our proposed model (1) applies regardless of the degrees-of-freedom of the compositional displacement problem, and (2) is guaranteed to yield a continuous relative-permeability function across the entire compositional space. We have implemented this model in our research simulator, and we present test cases using traditional relative-permeability models, as well as, numerical results that compare the nonlinear performance.

SummaryIn the development of fractured reservoirs, geomechanics is crucial because of the stress sensitivity of fractures. However, the complexities of both fracture geometry and fracture mechanics make it challenging to consider geomechanical effects thoroughly and efficiently in reservoir simulations. In this work, we present a coupled geomechanics and multiphase-multicomponent flow model for fractured reservoir simulations. It models the solid deformation using a poroelastic equation, and the solid deformation effects are incorporated into the flow model rigorously. The noticeable features of the proposed model are it uses a pseudocontinuum equivalence method to model the mechanical characteristics of fractures; the coupled geomechanics and flow equations are split and sequentially solved using the fixed-stress splitting strategy, which retains implicitness and exhibits good stability; and it simulates geomechanics and compositional flow, respectively, using a dual-grid system (i.e., the geomechanics grid and the reservoir-flow grid). Because of the separation of the geomechanics part and the flow part, the model is not difficult to implement based on an existing reservoir simulator. We validated the accuracy and stability of this model through several benchmark cases and highlighted the practicability with two large-scale cases. The case studies demonstrate that this model is capable of considering the key effects of geomechanics in fractured-reservoir simulation, including matrix compaction, fracture normal deformation, and shear dilation, as well as hydrocarbon phase behavior. The flexibility, efficiency, and comprehensiveness of this model enable a more realistic geocoupled reservoir simulation.

The main parameter that drives oil industry contract investment and set up economic feasibility study for approving field development plan is hydrocarbon reservoir potential. So a qualified experience should be deeply afforded to correctly evaluate hydrocarbons reserve by applying different techniques at each phase of field management, through collecting and using valid and representative data sources, starting from exploration phase and tune-up by development phase. Commonly, volumetric calculation is the main technique for estimate reservoir potential using available information at exploration stage which is quite few data; in most cases, this technique estimate big figure of reserve. In this study case, volumetric calculation estimate gas initial in place (GIIP) value almost two times bigger than other techniques estimation of actual reservoir potential; it is a result of Asphiltena “Bitumen” existing in reservoir interval which occupied part of matrix pore and fill some fractures. This investigation is raised up at early field production life: material balance calculation and run simulation analysis are applied to re-assessment and tune-up reservoir potential; both techniques are set up almost same GIIP value which principally tuned to actual reservoir dynamic energy behavior. Finally, material balance should be viewed as a complement to simulation, not as a competing approach, and using both to improve analysis of hydrocarbon reservoirs.

In South Korea, rainfall deficits lasting for three years (2014–2017), have decreased the reservoir storage level. The Boryeong reservoir, located in the Geum River western sea basin, recorded the lowest reservoir storage. The Boryeong water transfer project has supplied water resources to the east side of the basin. This is an emergency water supply project operated when there is a water supply shortage in the reservoir. It is essential to use appropriate settings for the operation scheme. In this study, a reservoir simulation model was built by applying a water supply adjustment standard, which reduces the water supply when reservoir storage is decreased to a specific level. Four operation cases (no operation, continuous operation, and two temporary operations) were used to analyze the effects of the project. The temporary operations activate when the reservoir storage decreases. The volumetric reliability of the water supply is enhanced by 1.8–2.2% in the temporary operation cases. This study used cost-benefit analysis to analyze the two temporary operation cases. Operating the transfer project when reservoir storage goes below a certain level is more efficient than other cases.

Recent experience in applying recurrent neural networks (RNNs) to interpreting Permannt Downhole Gauge (PDG) records has highlighted the potential utility of machine learning algorithms to learn reservoir behavior from data. The power of the RNN resides in its ability to retain information in a form of memory of previous patterns and information contained in the previous behavior of phenomena being modeled. This memory plays a role of informing the decision at the present time by using what happened in the past. This property suggests RNNs as a suitable choice to model sequences of reservoir information, even when the reservoir modeler is faced with incomplete knowledge of the underlying physical system. Convolutional Neural Networks (CNNs) are another variant of machine learning algorithms that have shown promise in sequence modeling domains, such as audio synthesis and machine translation. In this study, RNN and CNN were applied to tasks that traditionally would be modeled by a reservoir simulator. This was achieved by formulating the relationship between physical quantities of interest from subsurface reservoirs as a sequence mapping problem. In addition, the performance of a CNN layer as compared to RNN was evaluated systematically to investigate their capabilities in a variety of tasks of interest to the reservoir engineer. Preliminary results suggest that CNNs, with specific design modifications, are as capable as RNNs in modeling sequences of information and as reliable when making inferences to cases that has not been seen by the algorithm during training. Design details and reasons pertaining to the way these two seemingly different architectures process information, and handle memory are also discussed.

Understanding scaling of enhanced oil/bitumen recovery processes is essential in moving laboratory scale experimental results to field scale. Scaling theory for thermal processes is well understood and has been applied to steam processes. However, scaling of hybrid steam (thermal) /solvent (mass transfer) processes is still not well defined nor well understood. This paper investigates the scaling behavior of hybrid steam/butane gravity drainage processes using reservoir simulation (commercial thermal compositional simulator CMG STARSTM). Previous research has used reservoir simulation to confirm scaling groups for waterflooding. A similar strategy was used in this study whereby the scaling of a hybrid (steam) solvent oil recovery process was examined using reservoir simulations at three different reservoir scales: lab scale, semi-field scale and field scale to examine the influence of the mass transfer mechanisms of diffusion and dispersion on the scalability of the process. In particular, the influence of butane solvent concentration on scaling a steam/butane gravity drainage process was investigated by considering several butane mole fraction concentrations injected with steam (1%, 2%, 5%, 7%, 10%, 15%, 21%, 25% and 50%). Temperature contours, and mole fraction contours of butane in both the oil and gas phases were examined for various solvent injection concentrations to examine scalability. Numerical results are provided with no diffusion and dispersion, diffusion only, dispersion only and with both diffusion and dispersion added to the simulations. Results confirmed scalability of the process with no capillary effects when the simulation results were non-dimensionalized, although there were some issues with material balance errors in some of the simulation results particularly at high solvent concentrations. For low injection concentrations, the contours were almost identical (indicating scalability) for the three scales for the operating condition studied. In addition, capillary effects were also studied, and similar to scaling thermal processes, the capillarity effects influenced scalability of the process under the conditions studied particularly at higher injection concentrations. Scalability using reservoir simulation was generally preserved with low injection concentrations, but unusual behavior was observed at higher injection concentrations (>5%). Oil recovery curves were non-dimensionalized to make comparisons amongst the three scales. The oil recovery curves displayed an unusual S-shaped behavior at higher injection concentrations when capillary effects were included especially for the lab and semi-field scales. In all cases when all of the mechanisms are included (diffusion, dispersion and capillary effects), Scale 1 shows a much faster recovery than Scale 3 which suggests that the lab scale might temporally overestimate the field scale recovery for this particular process scenario.

The optimization of oil field development scheme considering the uncertainty of reservoir model is a challenging and difficult problem in reservoir engineering design. The most common method used in this regard is to generate multiple models based on statistical analysis of uncertain reservoir parameters and requires a large number of simulations to efficiently handle all uncertainties, thus requiring a huge amount of computational power. In order to reduce the computational burden, a method which combines reservoir simulation, an economic model, polynomial chaos expansion with response surface methodology, and Levy flight particle swarm optimization (LFPSO) algorithm is proposed to determine the optimal injection-production parameters with reservoir uncertainties at a reasonable computational cost. This approach is applied to a five-spot well pattern optimization design for obtaining the optimal parameters, including oil-water well distance, injection rate, and bottom hole pressure, while considering the uncertainties of porosity, permeability, and relative permeability. The results of the case study indicated that the integrated approach is practical and efficient for performing reservoir optimization with uncertain reservoir parameters.

Tight gas reservoirs are unconventional reservoir assets which have been the focus of major research in the petroleum industry owing to the global decline in conventional reservoirs. They are widely unlocked by creating hydraulic fractures in the formation to increase the flow capacity and productivity. The objective of this paper is to analyze different fracture geometries and their effect on tight gas production. The reservoir simulation model of the tight gas reservoir has been built with single porosity approach. A single vertical well with a single stage fracture has been used in the model to predict the behavior of fracture geometry. The major parameters of fracture geometry studied are fracture half-length, fracture width, and fracture height. Four sensitivities are run over different fracture geometry that is constant height and constant width, constant height and changing width, changing height and constant width, and changing height and changing width, while increasing the fracture half-length from 100 ft to 500 ft in each case. Sensitivity analysis exhibited that keeping the hydraulic fracture at constant height and constant width while increasing the fracture half-length resulted in enhanced tight gas productivity i.e. 11.63%, 14.14%, 16.06%, 17.48%, and 18.89% at hydraulic fracture half-lengths of 100 ft, 200 ft, 300 ft, 400 ft, and 500 ft, respectively, compared to other types of fracture geometry.

Gravimetric methods are used for monitoring the migration of CO2 stored in the reservoir. Reservoir permeability is crucial for injectivity and potentially important for gravity changes associated with CO2 storage. Nevertheless, its effects on gravity changes have not been investigated systematically. After conducting numerical simulations of CO2 injection and storage using models with varying permeability conditions (50–1000 mD), we calculated surface gravity changes. Results show that two mechanisms affect gravity decrease, related to differences in CO2 density and in CO2 spatial patterns. Under low‐permeability conditions, gravity decrease is minimal during injection because of increases in pressure and the resulting relative high density of CO2. Gravity decrease is enhanced after injection ceases because of the pressure release and resulting CO2 expansion. These effects are strong at shallow depths, at which CO2 is near the critical point and highly compressible. Under higher permeability conditions, rapid spreading of the CO2 plume causes smaller gravity reduction rate after some decades of injection. After injection ceases, upward CO2 migration because of buoyancy and plume spreading increase gravity above the injection zone. The time required for gravity change detection depends on permeability. Detection during injection can be made earlier under higher permeability conditions and under lower permeability conditions after injection ceases. These points suggest that permeability affects the feasibility of gravimetric methods. Moreover, designing monitoring programs based on permeability and depth conditions is necessary for successful monitoring. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

Invalid PVT data provide high uncertainty in field development studies such as reservoir fluid compartmentalization, reserve estimation, reservoir simulation, production engineering, and surface facility design. Then, consistency check should be conducted to ensure the validity of PVT data and to identify the most representative PVT sample(s). In this paper, preliminary and complementary consistency check of PVT laboratory methods was applied to design a new comprehensive validity check procedure for verification of the reservoir fluid properties. This procedure would be ascertained whether or not the data could be used as the most representative fluid sample in further studies. The data used in this study were collected from seven undersaturated oil reservoirs, and challenges were observed through the 28 full set of available PVT laboratory data. In our study quality checking procedure of PVT samples consists of different methods such as graphical method, recombination and material balance check for well stream composition test, Buckley, modified Wilson and Hoffman plot, Watson characterization factor, Y-function of CCE test, Y-function of DL test, compositional material balance of DL test, overall mass balance for density, Bo and GOR of DL test and density check for separator test. After implementing the applicable proposed checking procedure to detect the validity of laboratory PVT sample, only 9 out of 28 PVT samples were satisfied all the preliminary and complementary check methods. Furthermore, a practical strategy, to select representative reservoir fluid sample, was also provided and discussed based on the consistency check, well condition check and comparison of fluid properties.

Summary The reduction of pore pressure caused by depletion can induce significant reservoir compaction and loss of permeability, especially in unconsolidated reservoirs. In this paper, we develop a numerical approach on the basis of computer-based simulations of unconsolidated rock samples subjected to mechanical tests that replicate the one-dimensional (1D) strain depletion path and allow for a prediction of permeability loss. The 1D strain stress path is a good approximation for long and thin conventional reservoirs with a compliant caprock. The numerical sample consists of crushable stiff and soft grains (proxies for sand and shale) simulated with the discrete element method (DEM) coupled with the bonded-particle model (BPM). Model parameters are calibrated through numerical single-grain-crushing tests which reproduce the experimentally measured sand strength. Grain crushing induced by the uniaxial strain stress path results in a pronounced reduction of porosity and permeability, which manifests more readily for samples with large grain size. The change of particle-size distribution indicates that high effective stresses cause grain crushing and production of a significant amount of fines that lower permeability. Simulation results indicate that the presence of soft grains and inclusions (e.g., shale fragments) facilitates grain crushing. Reservoir simulations—incorporating the change of porosity and permeability as a compaction table—show that the compaction can enhance cumulative production due to compaction drive but also reduces production rate by impairing the reservoir permeability. This multiscale numerical workflow bridges grain-scale compaction behavior and field-scale reservoir production.

Summary Field-scale representation of highly heterogeneous reservoirs remains a challenge in numerical reservoir simulation. In such reservoirs, detailed geological models are important to properly represent key heterogeneities. However, high computational costs and long simulation run times make these detailed models unfeasible to use in dynamic evaluations. Therefore, the scaling up of geological models is a key step in reservoir-engineering studies to reduce computational time. Scaling up must be carefully performed to maintain integrity; both truncation errors and the smoothing of subgrid heterogeneities can cause significant errors. This work evaluates the latter—the effect of averaging small-scale heterogeneities in the upscaling process—and proposes a new upscaling technique to overcome the associated limitations. The technique is dependent on splitting the porous media into two levels guided by flow- and storage-capacity analysis and the Lorenz coefficient (LC), both calculated with static properties (permeability and porosity) from a fine-scale reference model. This technique allows the adaptation of a fine highly heterogeneous geological model to a coarse-scale simulation model in a dual-porosity/dual-permeability (DP/DP) approach and represents the main reservoir heterogeneities and possible preferential paths. The new upscaling technique is applied to different reservoir-simulation models with water injection and immiscible gas injection as recovery methods. In deterministic and probabilistic studies, we show that the resulting coarse-scale dual-permeability models are more accurate and can better reproduce the fine-scale results in different upscaling ratios (URs), without using any simulation results of the reference fine-scale simulation models, as some of the current alternative upscaling methods do.

Summary This paper advances the understanding of foam transport in heterogeneous porous media for enhanced oil recovery (EOR). Specifically, we investigate the dependence of methane foam rheology on the rock permeability at the laboratory scale and then extend the observations to the field scale with foam modeling techniques and reservoir simulation tools. The oil recovery efficiency of conventional gasflooding, waterflooding, and water-alternating-gas (WAG) processes can be limited by constraints such as bypassing effects (including both viscous fingering and channeling mechanisms) and gravity override. The problem can be more severe if the reservoir is highly fractured or heterogeneously layered in the direction of flow. Foam offers the promise to address the three issues simultaneously by better controlling the mobility of injected fluids. However, limited literature data of foam-flooding experiments were reported using actual reservoir cores at harsh conditions. In this paper, a series of methane (CH4) foam-flooding experiments were conducted in three different actual cores from a proprietary reservoir at an elevated temperature. It is found that foam rheology is significantly correlated with the rock permeability. To quantify the mobility control offered by foam, we calculated the apparent viscosity on the basis of the measured pressure drop at steady state. Interestingly, the apparent viscosity was found to be selectively higher in the high-permeability cores compared with that in the low-permeability zones. We parameterized our system using a texture-implicit-local-equilibrium model (STARS™ simulator, Computer Modelling Group, Calgary, Alberta, Canada) to illustrate the dependence of foam parameters on rock permeability. In addition, we created a two-layered model reservoir using an in-house simulator called modular reservoir simulator (MoReS; Shell Research, Rijswijk, The Netherlands) to elucidate the role of different driving forces for fluid diversion at the field level. We took into consideration the combined effect of gravitational, viscous force, and capillary forces in our simulation. We show that the gravitational forces prevent the gas from sweeping the lower part of the reservoir. However, the poor sweep can be ameliorated by intermittent surfactant injection to generate foam. In addition, the capillary force which hinders the gas (nonwetting phase) from entering the low-permeability region can be effectively leveraged to redistribute the fluids in the porous media, resulting in better sweep efficiency. We conclude that foam if properly designed can effectively improve the conformance of the WAG EOR in the presence of reservoir heterogeneity.

Relative permeability (kr) and the capillary pressure (Pc) are the central key elements defining the multiphase fluids flow behavior in the porous media. However, the dynamic capillarity should consider the dynamic relative permeability and the dynamic capillary pressure while performing waterflooding process in extremely low permeable formations. In order to improve the oil production, the advanced horizontal well drilling along with multiple hydraulic fracturing is generally instigated to penetrate the unconventional resources. The aim of this study is to consider the dynamic capillarity in a commercial reservoir simulation, while utilizing the data gained from the dynamic and steady experiments of the relative permeability and the capillary pressure impacts during waterflooding process in the core plugs of unconventional tight oil reservoirs. The commercial reservoir simulation conducted sensitivity analyses using Computer Modeling Group simulator. The outcomes show that the well production of the reservoir is overestimated while implementing steady data for forecasting due to which the oil saturation decreases more equally and further rapidly. Additionally, the forecast of the well production estimated to breakthrough sooner. However, neglecting the dynamic capillarity causes a huge breakthrough of water influx. Therefore, the core objective of this study is to probe the consequences of taking into consideration the dynamic capillarity in ultra-low permeable formations while giving an alternative perspective to forecast the production of the hydraulically fractured unconventional tight oil reservoirs.

Summary Fractured vuggy reservoir is a typical type of carbonate reservoir. The 3D complex fracture networks and Stokes flow inside vugs make fractured vuggy reservoir simulation remain a challenging problem. Most of the proposed models in previous studies are computation consuming, which cannot meet with the demand of field application. In this paper, a novel and efficient hybrid model, consisting of a modified embedded discrete fracture model (EDFM) and a vug model, is proposed to simulate multiphase flow in 3D complex fractured vuggy reservoirs. The modified EDFM improves the fracture-discretization process by using two sets of independent grids for matrix and fracture systems, which promotes the modeling of 3D complex fractures in real geological structures. Meanwhile, the vug model simplifies the coupled porous-free flow with the assumption of multiphase instantaneous gravity differentiation. The accuracy of the modified EDFM and the vug model is demonstrated by comparing the results with those of the conventional EDFM and volume of fluid (VOF) method. After that, a series of case studies, including three conceptual fracture-vug unit models and a real field model, have been conducted to test the proposed hybrid model. The results of the three fracture-vug unit models indicate the significant effect of a local fracture-vug structure on the flow characteristics and production performance. Finally, the application with a real field model with 3D complex fracture and vug geometries further verifies the practicability of our proposed model in real fractured vuggy reservoirs.

The increasing energy demand has to be met while we transitioned to a decarbonized energy future. Heavy oil and bitumen reserves are urgently needed to be developed to ensure that a smooth transition is provided. In this work, field-scale kinetics parameters are used to study the effect of reservoir pay thickness on the performance of toe-to-heel air injection (THAI) process. Air was injected at constant rate into three different models with the thicknesses of 24 m, 16 m, and 8 m, respectively. The oil produced is slightly affected by the reservoir thickness. It is found that the lower the reservoir thickness, the larger the cumulative air-to-oil ratio (cAOR), indicating that heat loss increases with the decrease in the reservoir thickness. This trend is similar to steam-based processes. At constant air injection flux, it is found that both the cumulative oil produced and the cAOR decrease with the decrease in the reservoir thicknesses. This decrease is attributed to the decrease in the rate of heat generation in the thinner reservoirs, which in turn results in lower combustion zone temperature and thus lower temperature gradients between the reservoir and the overburden and the reservoir and the underburden. Consequently, a more general conclusion is that decreasing the air injection rate by the same factor the reservoir thickness is decreased (i.e. keeping the air injection flux constant) results in a more economical THAI process operation compared to when the air injection rate is kept constant (i.e. allowing increase in air injection flux).

Simulations and seismic inversions exhibit good performance in reservoir modeling task for the steady performance of conventional techniques. However, they still hardly meet the high demand of petroleum exploration since the impossibility of reaching high resolution both in vertical and lateral directions. Furthermore, simulations can only provide high-resolution results near loggings, while seismic inversions usually contain band-limited problems. Therefore, we present the modified generative adversarial nets with a decoder (DeGAN) as a novel approach, which is integrated with sequential simulation to realize super-resolution reservoir simulation. Specifically, the proposed method provides a geological model with high vertical resolution and optimized by the Zeoppritz function, introducing logging and seismic data simultaneously. After resampling and warping, DeGAN can be trained by these data sets and supplies a structure to generate high-frequency parts for reconstructing a super-resolution simulation of a subsurface profile. The proposed method presents the three-flow architecture of DeGAN to balance the contributions of three neural network models and utilizes this strategy in an offshore area successfully. By introducing multiple data sets, density experiments demonstrate that this approach can provide density profile with super-resolution for revealing possible thin layers, and the frequency distribution is in accord with loggings. The positive result verifies the effectiveness of this approach for providing a super-resolution simulation to supply a solution to the problem of the band-limited profile in seismic inversion.

Drought has been frequently occurring in South Korea due to climate change. Analyzing the water supply capacity of the water resource system provides essential information for water resource management. This study evaluates the future water supply capacity of the Gwanghye (GH) agricultural reservoir based on the representative concentration pathways 4.5 and 8.5 climate change scenarios. We performed a reservoir simulation by reflecting the full water level of the reservoir before and after reservoir heightening. Climate change is expected to decrease the GH reservoir’s future available water resources due to the overall reduction in the reservoir’s runoff. After the reservoir-heightening project, an overall improvement was observed in the stability of the future irrigation water supply. Moreover, the remaining water after the supply of the irrigation water could supply 0.6–7.2 × 103 m3 of daily instream water. Thus, flexible reservoir operations are necessary according to climate change scenarios and the reservoir operation period. The use of climate change information should be expanded to establish reasonable water management policies for future climate change scenarios.

Summary Boscán is a giant multibillion-barrel heavy-oil (10.5 °API and asphaltic) field in Venezuela. Although a large part of the field is on primary production with a low recovery factor ( Water injection in such heavy oil was previously considered not effective by the industry because of adverse mobility ratio. However, water injection for pressure maintenance (WIPM) was successfully implemented using a new pattern configuration, a pseudo-1-3-1 inverted- spot pattern (an additional row of producers between conventional-pattern rows). Field-data and reservoir-simulation models show increased reservoir pressure up to the second row of producers from the injector. The pressure support is used to significantly improve recovery using the unique configuration at low water cut. WIPM has already resulted in significant reserves addition. Current production from water-injection areas is approximately 40,000 MBOPD (or approximately 47% of field production). However, it is estimated that because of high oil viscosity, a significant amount of oil remains bypassed in the WIPM area. An infill opportunity was identified from an integrated reservoir-management study that included detailed WIPM data analysis and dynamic (mechanistic and full-field) modeling. A unique infill configuration is proposed that conceptually uses the current injectors with an additional row of producers between the existing first and second row of the producers. This configuration has the potential to economically unlock millions of barrels of bypassed oil and significantly increase recovery in this prolific heavy-oil field. This study provides insights into HMRWF behavior, evaluating the relative effect of displacement vs. pressure. The unique and novel infill configuration can be used to improve recovery, a step vital to monetize this large resource in the low-price environment.

In this work, a flowing material balance equation (FMBE) is established for undersaturated coalbed methane (CBM) reservoirs, which considers immobile free gas expansion effect at the dewatering stage. Based on the established FMBE, five straight-line methods are proposed to determine the control area, initial water reserve, initial free gas reserve, initial adsorbed gas reserve, original gas in place, as well as permeability at the same time. Subsequently, the proposed FMBE methods for undersaturated CBM reservoirs are validated against a reservoir simulation software with and without considering free gas expansion. Finally, the proposed methods are applied in a field case when considering free gas expansion effect. Validation cases show that the straight-line relationships for the proposed five FMBE methods are excellent, and good agreements are obtained among the actual reserves and permeabilities and those evaluated by the proposed five FMBE methods, indicating the proposed five FMBE methods are effective and rational for CBM reservoirs. Results show that a small amount of free gas will result in a great deviation in reserve evaluation; hence, the immobile free gas expansion effect should be considered when establishing the material balance equation of undersaturated CBM reservoirs at the dewatering stage.

Summary The boundary-element method (BEM) is widely used in modeling fluid flow in fractured reservoirs. However, the computation is extremely expensive when real heterogeneity and large numbers of fractures are modeled. This paper presents a novel edge-based Green element method (GEM) (eGEM) for this problem, and two significant modifications are made to the classical GEM. An edge-based discretization scheme is proposed to improve accuracy of the GEM. The eGEM technique is further enriched for simulating discrete fractures. The mathematical model is transformed into the Laplace domain, which makes it convenient to incorporate multiporosity models because the form of the boundary integral equation is the same. The matrix is meshed using Cartesian grids, and discrete fractures are handled flexibly by embedding into the matrix grids. In eGEM, the matrix/matrix flow is coupled at the common edge, so the unknown flux can be eliminated by using the edge-based scheme. In each matrix block, the matrix/fracture flow is modeled by treating the fracture elements as sources or sinks, as with BEM. The finite-difference method (FDM) is used to handle the fracture/fracture flow. In this paper, we tested the numerical accuracy and computational efficiency of the eGEM using several cases. First, the technique was shown to have higher accuracy than the classical corner-based GEM for transient problems in the petroleum industry. This shows the advantage of the edge-based discretization approach in handling the unknown flux of each solution point. The ability of the eGEM to handle discrete fractures was validated with the several models for transient-flow problems. The computation of the proposed eGEM is much less expensive than that for BEM in modeling the transient behavior of fractured media. Compared with the commercial numerical simulator in handling discrete fractures, the eGEM is shown to be less grid sensitive and to maintain a relatively high precision even with coarse grids near the discrete fractures. A detailed grid-sensitivity analysis was performed. The fracture grids are recommended to be refined to capture the early-time-flow behavior in pressure-transient analysis, especially in modeling low-conductivity fractures. For the first time, an efficient edge-based discretization scheme for GEM is presented, which handles the unknown flux of each solution point and at the same time uses eGEM to enrich simulation of discrete-fracture networks. This method serves as a new efficient approach for reservoir simulation and numerical well testing. Because of the high precision of eGEM with coarse grids, it would be efficient in larger field applications.

Thermal recovery methods are used typically to produce heavy oil and bitumen. During this process, the temperature is raised to reduce oil viscosity. Conventional thermal methods may not be effective on deep wells or thin zones. An alternative to the thermal process is electromagnetic heating, which is a process based on the transformation of electric energy into thermal energy due to the electromagnetic field and electrically sensitive particles interacting on the medium. In this paper, reservoir simulations with similar characteristics to those found in the sedimentary basins of the Brazilian Northeast were performed. The purpose of this work is to analyze the impact of the number of fractures, fracture configurations and number of producer wells during electromagnetic heating over the oil recovery factor (ORF) value. The results revealed an oil production increment with increasing number of producer wells and when fractures are located at the center of the oil production zone. However, oil production did not show an evident increase connected to the number fractures.

Summary The enhanced oil recovery (EOR) technology can be instrumental in achieving the maximum rate of return from a hydrocarbon reservoir. One of the widely implemented EOR methodologies is the cyclic steam injection (CSI) which is a thermal recovery process aiming to reduce the oil viscosity and increase the production in naturally fractured heavy-oil reservoirs. However, commercial software used for CSI modeling can be difficult to learn and implement, also can be time-consuming and costly. This paper describes three artificial neural network (ANN) based models that have been trained for accurate and fast CSI performance evaluation with easy implementation. In this study, to model the CSI process, a commercial numerical model is used synchronously with the ANN models. Our goal is to discuss smart proxy models’ mimicking ability of the commonly used numerical models. Three ANN models have been trained with different network topologies, and transfer functions using a data set consists of 1,428 cases: Forward model: to predict performance indicators and viscosity contours. Inverse Model 1: to predict CSI design parameters. Inverse Model 2: to predict significant reservoir properties. The results from the trained ANN models have been compared with the results generated by the available commercial software. It was observed that ANN models can provide the results within a few seconds while it takes more than 30 minutes in some cases for the commercial software. The computational time for the numerical model being extensive, the number of trials to be conducted to find the optimum parameters for the CSI operations can be prohibitively expensive. The trained ANN-based models are capable of providing results within a rather low error margin. The developed ANN-based models are controlled by a user-friendly graphical user interface (GUI), which decreases the time expended on learning and executing the software.

An onshore gas field (hereafter called the R field—real name not revealed) is in the southeast coast of Tanzania which includes a Tertiary aged shaly sand formation (sand–shale sequences). The formation was penetrated by an exploration well R–X wherein no core was acquired, and there is no layer-wise published data of the petrophysical properties of the R field in the existing literature, which are essential to reserves estimation and production forecast. In this paper, the layer-wise interpretation of petrophysical properties was undertaken by using wireline logs to obtain parameters to build a reservoir simulation model. The properties extracted include shale volume, total and effective porosities, sand fractions and sand porosity, and water saturation. Shale volume was computed using Clavier equation from gamma ray. Density method was used to calculate total and effective porosities. Thomas–Stieber method was used to determine sand porosity and sand fraction, and water saturation was computed using Poupon–Leveaux model. The statistics of the parameters extracted are presented, where shale volume obtained that varies with zones is between 6 and 54% volume fraction, with both shale laminations and dispersed shale were identified. Total porosity obtained is in a range from 12 to 22%. Sand porosity varies between 15 and 25%, and sand fraction varies between 33 and 93% height fraction. Average water saturation obtained is between 32 and 49% volume fraction. Open AccessThis article is licensed under a Creative Commons Attri-bution 4.0 International License, which permits use, sharing, adapta-tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0

The ability to determine a predictive stability criterion is of great practical importance for designing stable polymer-based displacements. Where one usually resorts to a limited number of core-scale experiments or coarse-scale reservoir simulations, the first ones are potentially impacted by length scale issues, while the second ones possibly smooth out sharp displacing fronts and physical instability due to numerical diffusion. This paper proposes a new hydrodynamical stability criterion based on the previous linear stability analysis results. This criterion is tested for 2D polymer oil displacement by performing high viscosity contrasts, high-resolution numerical experiments at pilot scale. We investigate mesh resolution issues and several perturbation ideas. Different factors are considered such as mobility ratios, polymer adsorption and degradation and heterogeneities. The analysis is based on a combination of reservoir simulation and image processing techniques. We show the development of viscous fingering in homogeneous porous media is driven by the shock mobility ratio defined as the ratio of the total fluids upstream mobility over the total fluids downstream mobility. This stability criterion proves to predict both the polymer upstream and polymer-free downstream saturation fronts stability, typical of a polymer displacement, whether polymer adsorbs on the rock or degradates, or not. The observed fingers dynamical behavior is in line with previous works addressing single-phase miscible flow or immiscible oil displacement in porous media: fingers transversally merge while growing in the flow direction. Time evolution of fingers spreading and number is linear. Investigation on porous media of variable heterogeneity distributions shows how viscous fingering couples with heterogeneity and leads to even more marked, distorted and unstable flow patterns. In those cases, flow patterns are not solely driven by the porous medium heterogeneity. The more unstable the flow is, the more sensitive it is to heterogeneity. In-depth fingers analysis shows a very specific time evolution behavior, quite different from viscous fingering in homogeneous media. Such a flow pattern is related to production data such as water and polymer breakthrough times and/or oil recovery profiles as a function of time, which can be used in turn to interpret displacement stability and porous medium heterogeneity features.

Summary In this study we compare real data from an Eagle Ford Shale huff ‘n’ puff (H&P) gas-injection pilot with reservoir simulation and tank material-balance calculations. The comparison is good and supports the conclusion that oil recovery from the Eagle Ford (and likely other shales) can be increased significantly using H&P. For H&P to work, the injected gas and the in-situ oil in the shale must be contained vertically and laterally following hydraulic fracturing. Containment is critical for the success of H&P. Containment implies that the injected gas flows into the hydraulic fractures, penetrates the tight matrix, and does not escape or leak outside the target stimulated reservoir volume (SRV). Vertical and lateral containment exists in the Eagle Ford as demonstrated previously (Ramirez and Aguilera 2016) with an upside-down distribution of fluids: Natural gas is at the bottom of the structure, condensate in the middle, and oil at the top. Two different matching and forecasting approaches are used in this study: reservoir simulation and tank-material-balance calculations. The results show a good history match of primary recovery and secondary recovery by H&P in the pilot well. The history match is good in the case of both reservoir simulation and tank material-balance calculations. Once a match is obtained, the simulation and material balance are used to forecast secondary recovery over a period of 10 years with sustained H&P injection of dry gas. The results indicate that dry-gas H&P can increase oil recovery from the Eagle Ford Shale significantly. Under favorable conditions, oil recovery can be doubled and even tripled over time compared with the primary recovery. The addition of heavier ends to the H&P gas injection can increase oil recovery even more, putting it on par with recoveries in conventional reservoirs. The benefit of H&P occurs in the case of both immiscible and miscible gas injection. The H&P benefits can likely be also obtained in other shale reservoirs with upside-down containment of dry gas, condensate, and oil. The novelty of this work is the combined use of reservoir simulation and tank material-balance calculations to match the performance of an H&P gas-injection pilot in the Eagle Ford Shale of Texas. We conclude that oil recoveries can be increased significantly by H&P.