Search

Management of oil reservoirs requires many high computational cost simulations for decision-making. This work aims to develop fast machine learning-based proxies based on production history data to replace the reservoir simulator. The proxy must accurately forecast reservoir responses by analyzing time series of fluid phase rates or bottom hole pressures, defined as a function of informed controls of the injector and producer wells. It is crucial to note that this is not a regular problem of forecasting the future of time series based on past trends but the prediction of reservoir response time series as a function of informed new controls. The proposed strategy is to use neural networks to learn reservoir dynamics based only on samples of production history data. Multi-head architectures based on recurrent neural networks (RNNs), convolutional neural networks (CNNs) configurations, and hybridization are considered. Parallel architectures involving separate RNNs are tested, using long short-term memory (LSTM) and CNN concatenated sub-networks. Eight different architectures are tested on two reservoir models of different sizes and heterogeneity complexities. Several training samples are generated to assess their impact on accuracy and precision. For the smaller example, the CNN architecture produced the most precise results with fewer trainable parameters. The proposed Parallel CNN-LSTM architecture is generally the most successful for the larger and more complex reservoir. In any case, the proposed techniques have been demonstrated to be very promising, with root mean square error in the order of 2.25%. [ABSTRACT FROM AUTHOR]

This study aims to formulate a steady-state mathematical model for a three-dimensional permeable enclosure (cavity) to determine the oil extraction rate using three distinct nanoparticles, SiO2, Al2O3, and Fe2O3, in unconventional oil reservoirs. The simulation is conducted for different parameters of volume fractions, porosities, and mass flow rates to determine the optimal oil recovery. The impact of nanoparticles on relative permeability (k r) and water is also investigated. The simulation process utilizes the finite volume ANSYS Fluent. The study results showed that when the mass flow rate at the inlet is low, oil recovery goes up. In addition, they indicated that silicon nanoparticles are better at getting oil out of the ground (i.e., oil reservoir) than Al2O3 and Fe2O3. Most oil can be extracted from SiO2, Al2O3, and Fe2O3 at a rate of 97.8%, 96.5%, and 88%, respectively. [ABSTRACT FROM AUTHOR]

CO2 capture， utilization， and storage （CCUS） can achieve a win-win situation of carbon emission reduction and oil production increase， and it is a key technology for the green and low-carbon transformation of the fossil energy industry. The petroleum and petrochemical industries have accelerated the large-scale application of CCUS technology after China proposed the goals of “peaking carbon emissions before 2030 and achieving carbon neutrality before 2060” in September 2020. It has gradually been revealed that CO2 flooding has contradictions such as a low impact， short effective time， and rapid increase in the gas-oil ratio in the field practices of SINOPEC Shengli Oilfield and CNPC Jilin Oilfield. It is necessary to strengthen the optimization design of reservoir engineering further. This article systematically analyzed field practices of CO2 flooding to enhance oil recovery in sandstone reservoirs in China and abroad and key issues in the optimization design of reservoir engineering， such as the matching between reservoir pressure maintenance level and miscible pressure， between well pattern direction and formation stress direction， between well spacing and reservoir permeability， and optimization of gas-water alternative displacement methods combined with the research results of the million-ton CCUS demonstration project in Shengli Oilfield. Then， the article summarized the latest progress in reservoir engineering research， such as reservoir pressure maintenance level during CO2 flooding， well network， well spacing， and gas-water alternative displacement， and analyzed the increasing pressure method， well pattern， well spacing， and gas-water alternative displacement optimization design for high-pressure miscible flooding. Finally， it indicated that the research direction of CO2 flooding reservoir engineering design should focus on the phase and flow laws of crude oil， CO2， and water in porous media under high-pressure conditions， the synergistic mechanism of CO2 flooding and storage， and the gas channeling mechanism of CO2 flooding under complex reservoir conditions. This article provides technical support for large-scale CO2 flooding in China.

Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In today's industry, H2 is mostly produced from fossil fuels such as natural gas (NG), oil, and coal through various processes. However, all these processes produce both carbon dioxide (CO2) as well as H2, making them questionable in terms of climate change mitigation efforts. In addition to efforts to increase the conversion efficiency of green H2 technologies, work is also underway to make H2 production from fossil fuels more environmentally friendly by reducing/avoiding CO2 emissions. In this framework, these technologies are combined with geologic carbon storage. In a further step, the use of depleted hydrocarbon reservoirs for in situ H2 production is being investigated, with the co-generated CO2 remaining permanently in the reservoir. The objective of this paper is to provide a brief overview of the technologies that can be used to produce H2 from depleted and depleting hydrocarbon reservoirs (DHRs) in various ways. We evaluate the required processes from a reservoir engineering perspective, highlighting their potential for H2 generation and their technology readiness level (TRL) for applications. We also investigate the possibility of permanently storing the co-produced CO2 in the reservoir as a means of mitigating emissions. In addition, we provide a preliminary cost analysis to compare these methods with conventional hydrogen production techniques, as well as an assessment of operational risks and associated cost estimates. [ABSTRACT FROM AUTHOR]

When considering multiphase flow scenarios, the interpretation of petrophysical properties poses significant challenges for production forecasts and reservoir modeling. The findings of the numerical modeling were therefore subject to uncertainty because characteristics like relative permeability and capillary pressure curve were hardly ever bound by interpretations. The uncertainty may result in inaccurate predictions of reservoir performance and skewed perceptions of the reservoir. Due to the difficulty in directly interpreting such property from the available field data and the expensive cost of coring, analyses or experimental measurements to determine relative permeability and capillary pressure were infrequently carried out. Such a gap would be filled by a straightforward yet rigorous method. In this study, we develop production projections for a wide range of three-phase compositional volatile oil reservoirs. Then, we used an artificial neural network to figure out how petrophysical characteristics and production data relate to one another. The artificial neural network model was adjusted, and the final trained model was tested blindly to determine how well it predicted permeability, multiphase relative permeability, and capillary pressure data. For the testing scenarios, consistency is seen between the predicted values and the original ones, despite some mispredictions being present. To provide production projections that can be compared to those from the reservoir model that include the initial petrophysical characteristic, the anticipated properties are then propagated into reservoir models. The comparison findings show that for 65/59/34 out of 74 testing scenarios, the reservoir model with artificial neural network-predicted features can anticipate oil/gas/water output with < 20% inaccuracy. With the developed artificial neural network tool, the reservoir engineers can evaluate the three-phase relative permeability surface from rate-transient data conveniently improving the accuracy of the relative permeability data implemented by history matching or from core experiments which sometimes are extremely expensive. The findings of this study can help for a better understanding of the relationships between three-phase rate-transient data and the relative permeability surface as well as the horizontal/vertical permeability. [ABSTRACT FROM AUTHOR]

The mechanical formation damage induced by pore collapse within production curve depletion-dependent reservoirs significantly influences oilfield development. This paper proposes a new perturbative solution for transient pores collapse hysteresis modeling in depletion-dependent oil reservoirs with compaction effects during alternating loading/unloading cycles. The nonlinear hydraulic diffusivity equation is perturbed through a first-order expansion technique using the depletion-dependent permeability, k(p) as a perturbation parameter, ϵ . The practical uses of the model developed in this work are identifying flow regimes and hysteresis responses in pressure-sensitive reservoirs, estimating buildup pressure, specifying oil flow rate to prevent severe hysteretic behavior, and history matching during reservoir surveillance. The log–log analysis shows that the shut-in pressure has an influence on permeability loss. However, the comparisons between the permeability loss and its partial recovery curves show that this loss represents less than 5 % of the permeability value from the previous drawdown cycle. The derivative was also used to compute the instantaneous permeability loss using the relationship: ∂ m D / ∂ t D = k D (p D) ∂ p D / ∂ t D . The main advantages of the solution derived in this work are the simple implementation, practical graphical analysis of the pores collapse hysteresis effect, the possibility of simulating different boundary conditions and well-reservoir settings, and the requirements of only a few pressure and permeability field data to input in the deviation factor. The solution proposed can be applied to choose the production time to shut the well and monitor the adequate oil flow rate during the production curve. [ABSTRACT FROM AUTHOR]

To help determine in what ways virtual reality (VR) technologies may benefit reservoir engineering workflows, we conducted a usability study on a prototype VR tool for performing reservoir model analysis tasks. By leveraging the strengths of VR technologies, this tool’s aim is to help advance reservoir analysis workflows beyond conventional methods by improving how one understands, analyzes, and interacts with reservoir model visualizations. To evaluate our tool’s VR approach to this, the study presented herein was conducted with reservoir engineering experts who used the VR tool to perform three common reservoir model analysis tasks: the spatial filtering of model cells using movable planes, the cross-comparison of multiple models, and well path planning. Our study found that accomplishing these tasks with the VR tool was generally regarded as easier, quicker, more effective, and more intuitive than traditional model analysis software while maintaining a feeling of low task workload on average. Overall, participants provided positive feedback regarding their experience with using VR to perform reservoir engineering work tasks, and in general, it was found to improve multi-model cross-analysis and rough object manipulation in 3D. This indicates the potential for VR to be better than conventional means for some work tasks and participants also expressed they could see it best utilized as an addition to current software in their reservoir model analysis workflows. There were, however, some concerns voiced when considering the full adoption of VR into their work that would be best first addressed before this took place.

Copyright of Oil Drilling & Production Technology / Shiyou Zuancai Gongyi is the property of Shiyou Zuancai Gongyi Bianjibu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most of oil reservoirs in the world have faced decrease in production and they are in the second half of their life cycle. Therefore, tertiary and enhanced oil recovery (EOR) techniques are needed for continuous production from these reservoirs. As choosing the most appropriate EOR methods for a reservoir is a challenging task for reservoir engineers, screening of EOR approaches is of high importance before any full field simulation and experiments. Enhanced oil recovery screening is a multiple criteria decision-making (MCDM) problem and hence, a systematic statistical algorithm based on MCDM can be used for this purpose. In this study, for the first time, a new EOR screening method is proposed by using VIKOR and Monte-Carlo algorithms. The approach used a large database of successful EOR projects around the world and was applied to 12 various EOR methods including a wide range of conditions and properties. Pre-processing was performed on the gathered database and then based on reservoir engineering analyses and using a pairwise comparison matrix, initial weights were considered for the parameters in each EOR method. Afterward, these weights were used in the proposed VIKOR MCDM calculation algorithm and the corresponding numerical values of EOR techniques for each reservoir were obtained. Finally, the EOR method with the highest corresponding value was selected as the most suitable method. Results demonstrated that by using the presented approach, a high classification accuracy of 98% was obtained for different cases, which shows the proficiency and robustness of the developed screening algorithm. In addition, the reliability of the developed method was validated using data obtained from 11 oil reservoirs in the southwest of Iran. Also, the results were compared with the results of previous studies and they were in a very good match. The developed approach is less expensive and faster than full field simulation method and can be used as an efficient EOR screening approach for reservoirs with different properties in the world. [ABSTRACT FROM AUTHOR]

On the path to a net-zero-carbon economy, large-scale energy storage will be an essential part of a renewable energy system. Hydrogen generated from renewable or zero-carbon sources has significant potential as an energy storage medium, by allowing for long-duration and high-capacity energy storage. At the volumes anticipated, however, underground storage systems will likely be essential. Much like the current natural gas system, underground hydrogen storage would provide a safe and cost-effective complement to smaller surface storage infrastructure. Recently, there has been significant interest in the possibility of converting existing gas storage systems to hydrogen storage, though many uncertainties exist in such a process. This paper explores the reservoir dynamics of natural gas/hydrogen gas mixtures and the resulting impacts on storage conversion and gas deliverability. Two specific topics are addressed. First, a novel viscosity model for hydrogen-containing gas mixtures is developed, which shows better agreement with experimental measurements than the widely-used Lohrenz-Bray-Clark model. Second, detailed reservoir simulations are used to investigate cost-effective injection/withdrawal strategies for converting a natural gas storage system to one that stores hydrogen/natural-gas blends. The critical role of buoyancy and gravity override is described, and recommendations are made regarding key reservoir characteristics and injection strategies for managing this behavior. • This study focuses on the conversion of an operational natural gas storage field to hydrogen storage. • A novel viscosity model is developed for hydrogen-containing gas mixtures. • Reservoir simulations evaluate the effect of geologic and engineering parameters on composition and rate of recovered gas. • Gravity segregation significantly influences the severity of fluctuations in the produced gas composition. [ABSTRACT FROM AUTHOR]

Abstract Most of oil reservoirs in the world have faced decrease in production and they are in the second half of their life cycle. Therefore, tertiary and enhanced oil recovery (EOR) techniques are needed for continuous production from these reservoirs. As choosing the most appropriate EOR methods for a reservoir is a challenging task for reservoir engineers, screening of EOR approaches is of high importance before any full field simulation and experiments. Enhanced oil recovery screening is a multiple criteria decision-making (MCDM) problem and hence, a systematic statistical algorithm based on MCDM can be used for this purpose. In this study, for the first time, a new EOR screening method is proposed by using VIKOR and Monte-Carlo algorithms. The approach used a large database of successful EOR projects around the world and was applied to 12 various EOR methods including a wide range of conditions and properties. Pre-processing was performed on the gathered database and then based on reservoir engineering analyses and using a pairwise comparison matrix, initial weights were considered for the parameters in each EOR method. Afterward, these weights were used in the proposed VIKOR MCDM calculation algorithm and the corresponding numerical values of EOR techniques for each reservoir were obtained. Finally, the EOR method with the highest corresponding value was selected as the most suitable method. Results demonstrated that by using the presented approach, a high classification accuracy of 98% was obtained for different cases, which shows the proficiency and robustness of the developed screening algorithm. In addition, the reliability of the developed method was validated using data obtained from 11 oil reservoirs in the southwest of Iran. Also, the results were compared with the results of previous studies and they were in a very good match. The developed approach is less expensive and faster than full field simulation method and can be used as an efficient EOR screening approach for reservoirs with different properties in the world.

In today’s industry, H2 is mostly produced from fossil fuels such as natural gas (NG), oil, and coal through various processes. However, all these processes produce both carbon dioxide (CO2) as well as H2, making them questionable in terms of climate change mitigation efforts. In addition to efforts to increase the conversion efficiency of green H2 technologies, work is also underway to make H2 production from fossil fuels more environmentally friendly by reducing/avoiding CO2 emissions. In this framework, these technologies are combined with geologic carbon storage. In a further step, the use of depleted hydrocarbon reservoirs for in situ H2 production is being investigated, with the co-generated CO2 remaining permanently in the reservoir. The objective of this paper is to provide a brief overview of the technologies that can be used to produce H2 from depleted and depleting hydrocarbon reservoirs (DHRs) in various ways. We evaluate the required processes from a reservoir engineering perspective, highlighting their potential for H2 generation and their technology readiness level (TRL) for applications. We also investigate the possibility of permanently storing the co-produced CO2 in the reservoir as a means of mitigating emissions. In addition, we provide a preliminary cost analysis to compare these methods with conventional hydrogen production techniques, as well as an assessment of operational risks and associated cost estimates.

Machine learning techniques have garnered significant attention in various engineering disciplines due to their potential and benefits. Specifically, in reservoir numerical simulations, the core process revolves around solving the partial differential equations delineating oil, gas, and water flow dynamics in porous media. Discretizing these partial differential equations via numerical methods is one cornerstone of this simulation process. The synergy between traditional numerical methods and machine learning can enhance the precision of partial differential equation discretization. Moreover, machine learning algorithms can be employed to solve partial differential equations directly, yielding rapid convergence, heightened computational efficiency, and accuracies surpassing 95%. This manuscript offers an overview of the predominant numerical methods in reservoir simulations, focusing on integrating machine learning methodologies. The innovations in fusing deep learning techniques to solve reservoir partial differential equations are illuminated, coupled with a concise discussion of their inherent advantages and constraints. As machine learning continues to evolve, its conjunction with numerical methods is poised to be pivotal in addressing complex reservoir engineering challenges. [ABSTRACT FROM AUTHOR]

Copyright of Oil Drilling & Production Technology / Shiyou Zuancai Gongyi is the property of Shiyou Zuancai Gongyi Bianjibu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • Technology differences between hydrothermal and petroleum resources were analyzed. • Technologies in exploration, drilling and exploitation were reviewed and compared. • Electromagnetic method is more used for geothermal exploration than seismic method. • Geothermal wells have large borehole radii with open holes and high corrosion. • Numerical simulation as a useful tool for hydrothermal exploitation and optimization. The exploration, drilling, and development of high-temperature hydrothermal geothermal resources usually borrow ideas from the oil and gas industry. However, some techniques in the oil and gas industry are not entirely applicable for geothermal resources due to the difference in geological conditions and exploitation objectives. This paper briefly compared and analyzed the differences between high-temperature hydrothermal resources and oil & gas resources in exploration, drilling, and reservoir engineering to improve subsequent hydrothermal exploitation efficiency. The exploration of geothermal resources aims to identify high-temperature rock/fluid, and the improved electromagnetic method is mostly adopted to identify geological structures around geothermal resources due to its low cost. As for the seismic exploration primarily used in the oil & gas industry, it is difficult to interpret the rock associated with high-temperature geothermal activity, except of the high cost and time-consuming. Different from oil and gas wells, geothermal wells have large borehole radii and high requirements for casing corrosion resistance. Low-density drilling fluid is often used due to the lost circulation. Open holes with liner mostly complete conventional geothermal wells. For the dynamic development of geothermal resources, well test in the oil and gas industry can be used, but the material balance and empirical method in oil reservoir engineering are no longer suitable for dynamic analysis of geothermal resource development, and the real-time numerical simulation combined with field data is a more effective method for geothermal resource development. [ABSTRACT FROM AUTHOR]

A real-time and reliable prediction method for the onset of a steam breakthrough in heavy oil wells is proposed by integrating deep learning algorithms with dynamic data from the wells. To address the issues such as frequent fluctuations and high noise level in single indicator data, and inability to accurately characterize the steam breakthrough time, a set of indicator parameters is constructed based on the actual injection and production data of the oilfield, effectively representing the formation time of the steam channel. These parameters are combined with the variation coefficient-G1 hybrid cross-weighting method to fuse into a comprehensive breakthrough identification curve of steam channeling. On this basis, suitable time series data are selected as input features using the similarity measurement method based on standard mutual information, with the corresponding breakthrough identification curve as the output time series for constructing the learning samples. A sequence-to-sequence deep learning framework is used to establish a prediction model for steam breakthrough time, and the effectiveness and superiority of the model are verified by the actual predictions and comparison with the traditional machine learning methods. This method simulates the mapping relationship between the characteristics of injection production time series and the identification curve of steam channeling in a data-driven way, which can effectively improve the efficiency and accuracy of steam channeling time prediction, and has certain guiding significance for the intelligent early warning of steam channeling. [ABSTRACT FROM AUTHOR]

A hybrid cavity magnomechanical system to transfer the bipartite entanglements and achieve the strong microwave photon–phonon entanglement based on the reservoir engineering approach is constructed. The magnon mode is coupled to the microwave cavity mode via magnetic dipole interaction and to the phonon mode via magnetostrictive force (optomechanical‐like). It is shown that the initial magnon‐phonon entanglement can be transferred to the photon‐phonon subspace in the case of these two interactions cooperating. In the reservoir‐engineering parameter regime, the initial entanglement is directionally transferred to the photon‐phonon subsystem, so a strong bipartite entanglement in which the magnon mode acts as the cold reservoir to effectively cool the Bogoliubov mode delocalized over the cavity and the mechanical deformation mode is obtained. Moreover, dual‐mode cooling is realized by engineering the dissipation of photon and phonon modes within the target mode, which allows entanglement to be further enhanced. The results indicate that the steady‐state entanglement is robust against temperature. The dual‐mode cooling reservoir engineering scheme can potentially be extended to other three‐mode quantum systems. [ABSTRACT FROM AUTHOR]

This research was conducted to determine how the incorporation of different poroelastic equations would affect the measured rock matrix bulk modulus in the laboratory. To do this, three experimental methods were used to measure the matrix bulk modulus, K s , of seven sandstone specimens taken from the Świętokrzyskie mine in Poland. Those experimental methods were based on the different governing equations in poroelasticty theory. The matrix bulk modulus has a substantial impact on the rock strength against external stresses. Moreover, the rock bulk modulus depends directly on two components: the pore fluid bulk modulus and matrix bulk modulus. The second one is more important as it is much higher than the first one. In this study, the accuracy of those three methods in the measurement of the matrix bulk modulus was evaluated. For this purpose, an acoustic wave propagation apparatus was used to perform the required tests. For each method, an empirical correlation was extracted between the matrix bulk modulus and the applied hydrostatic stress. In all the experiments, an exponential correlation was observed between the matrix bulk modulus and the hydrostatic stress applied on the rock. Furthermore, it was found that the incorporation of the dry bulk modulus in the calculations led to an underestimation of the matrix bulk modulus. In addition, as the hydrostatic stress was raised, the matrix bulk modulus also increased. The applied methodology can be deployed to determine the matrix bulk modulus in coupled rock-fluid problems such as reservoir depletion, hydraulic fracturing, oil recovery enhancement, underground gas storage and land subsidence. [ABSTRACT FROM AUTHOR]

Understanding the fluid flow and heat transfer process in the well-fracture-reservoir combined system is crucial for the development of Enhanced or Engineered Geothermal Systems (EGS). In this paper, a well-fracture-reservoir integrated numerical model is developed to study the heat extraction performance considering the interactions among the well boreholes, the fractures in the reservoir and the base rock reservoir itself. The wells and major fractures are precisely described by the direct modelling while the sub-fractures in the base rock are described by a permeability distribution. A novel method to improve the heat extraction performance of EGS by enhancing the permeability in the near-well region is proposed. Simulation results show that the fluid flow path is predominantly determined by the well layout and fracture network. The fluid pressure is non-linearly distributed between the injection and production wells in the fractures and reservoir. Further, an optimized scheme for heat extraction of an assumed geothermal site in Gonghe Basin is proposed based on the near-well enhancement approach. The overall heat extraction performance of the near-well enhanced case is improved by more than 15.0%. The present work provides a feasible improvement scheme for EGS to achieve a better heat extraction performance. [ABSTRACT FROM AUTHOR]

The charging process of a quantum battery (i.e., a two‐level system) is studied in two scenarios, that is, a quantum battery and a charger are coupled to the common reservoir environments or coupled to their respective independent environments. In the common reservoir scenario, it is shown that the optimal charging process can be realized by increasing the number of environments and setting the same coupling strength between battery‐reservoirs and charger‐reservoirs. In the independent reservoir scenario, however, it is shown that decreasing either the number of reservoir environments or the coupling strength would enhance the charging performance. These results demonstrate that the charging performance of quantum batteries can be significantly improved by constructing common environments. This may be of help to the realization of the quantum battery with optimal charging performance in multiple environments. [ABSTRACT FROM AUTHOR]

Machine learning techniques have garnered significant attention in various engineering disciplines due to their potential and benefits. Specifically, in reservoir numerical simulations, the core process revolves around solving the partial differential equations delineating oil, gas, and water flow dynamics in porous media. Discretizing these partial differential equations via numerical methods is one cornerstone of this simulation process. The synergy between traditional numerical methods and machine learning can enhance the precision of partial differential equation discretization. Moreover, machine learning algorithms can be employed to solve partial differential equations directly, yielding rapid convergence, heightened computational efficiency, and accuracies surpassing 95%. This manuscript offers an overview of the predominant numerical methods in reservoir simulations, focusing on integrating machine learning methodologies. The innovations in fusing deep learning techniques to solve reservoir partial differential equations are illuminated, coupled with a concise discussion of their inherent advantages and constraints. As machine learning continues to evolve, its conjunction with numerical methods is poised to be pivotal in addressing complex reservoir engineering challenges.

The relevance. Polymer flooding is a technology that makes it possible to increase the efficiency of the development of existing oil and gas fields. At the same time, the implementation of the polymer flooding project requires additional investments. To justify additional investments and assess the economic efficiency of the entire project, a comprehensive methodology for evaluating the results of the application of polymer flooding technology, covering all elements of a complex system, is needed. The main aim of the research is investigation of the main constituent elements of the polymer flooding project; development of methodology for evaluating the efficiency of polymer flooding technology application. Objects of the research are unconsolidated viscous oil reservoir under development, as well as traditional reservoirs. Methods: critical analysis, content analysis, analysis, generalization and systematization of the existing experience of polymer flooding pilot projects, systematic approach. Results. The methodology for evaluating efficiency of polymer flooding technology implementation was developed, including a detailed assessment of the revenue side using analytical and numerical tools, a comprehensive assessment of the cost side and assessment of the economic efficiency of the entire project. Attention is paid to the assessment of incremental oil production using the existing methods at various stages of the project. The impact on operating costs is also shown. The main cost items in the implementation of polymer flooding were conceptually investigated and described. The technique is applicable at various stages of polymer flooding implementation from the planning stage of the pilot project and evaluation of its results after completion to the full-field implementation stage. The developed methodology makes it possible to systematically and comprehensively evaluate the effectiveness of polymer flooding projects. The technique is universal and equally suitable for both pilot projects and full-field implementation projects.

Copyright of Special Oil & Gas Reservoirs is the property of Special Oil & Gas Reservoirs Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nanomaterials find several applications in the oilfield operations. In oilwell drilling, nanomaterials are being used for mitigating wellbore instability conditions, improving thermal stability of the fluid, minimizing formation damage by preventing fluid invasion into the reservoir, improving fluid rheology, and controlling fluid loss. A wide range of nanomaterials from organic as well as inorganic and composites are being developed to accomplish the mentioned functions. Significant nanomaterials being developed include nanocellulosic particles, nanoclay, nanosilica, nanometals, nanometal oxides and nanopolymers. Improving fluid properties to develop smart fluids by the incorporation of nanomaterials is an emerging research field which is yielding promising results. This review article presents the recent development made in the applications of the nanomaterials in drilling fluids especially the nanosilica particles surface modified by organic ligands. Surface modification of nanosilica with organic ligands namely 3‐Glycidoxypropyl‐triethoxy silane (GPTS), hyperbranched polyethyleneimine (HPEI) and Moringa oleifera cationic protein (MOCP) are discussed. Applications of nanomaterials in drilling fluids to minimize wellbore instability, prevent formation damage, improve thermal stability and conductivity, stabilize fluid rheology, improve hole cleaning, and minimize the chances of downhole string sticking have been reviewed. [ABSTRACT FROM AUTHOR]

Compositional reservoir simulation is an important tool to model fluid flow in oil and gas reservoirs. Important investment decisions regarding oil recovery methods are based on simulation results, where hundred or even thousand of different runs are performed. In this work, a new methodology using artificial intelligence to learn the thermodynamic equilibrium is proposed. This algorithm is used to replace the classical equilibrium workflow in reservoir simulation. The new method avoids the stability test for single-phase cells in most cases and provides an accurate two-phase flash initial estimate. The classical and the new workflow are compared for a gas-oil mixing case, showing a simulation time speed-up of approximately 50%. The new method can be used in compositional reservoir simulations.

Underground hydrogen storage will be essential to a hydrogen economy. This work focuses on the challenge of identifying and screening candidate storage systems, given the unique behavior of hydrogen in the subsurface. Here, we describe a resource assessment methodology and apply it to Alaska's Cook Inlet region. Alaska provides an interesting case study because of its abundant renewable energy resources, relatively low energy demand, and isolated electrical grid. The assessment framework considers each site's ability to (1) store a specific volume, (2) physically-contain the stored gas, and (3) limit biogeochemical activity. We estimate that reservoirs in the Cook Inlet area could theoretically store a total of 286 TWh (or 8.6 million tonnes [Mt]) in hydrogen working gas in 92 pools. This is likely sufficient to meet both local hydrogen demand and support an array of exportable products. We further identify seven pools that may be especially well-suited for hydrogen storage. Broadly, this work demonstrates a framework for regional resource assessments. On a finer scale, this work supports an early demonstration of porous–media hydrogen storage in the United States. • Underground hydrogen storage will be critical to clean hydrogen deployment. • This work presents a reproducible framework for storage resource assessment. • 286 TWh of hydrogen-based energy could be stored in Cook Inlet reservoirs. • Seven pools are identified as especially favorable for hydrogen storage. • This storage could meet both local demand and support hydrogen exports. [ABSTRACT FROM AUTHOR]

The classic approach in engineering education starts with basic definitions, principles, mathematical modeling and formulation of an individual course and is well documented in textbooks. New engineering problems are normally beyond a course or an engineering area and graduates need to involve in a multidisciplinary team to analyze and solve these problems. To prepare for this team collaboration, students need to get familiar with an integrated education and research approach during their studies, as well as sensitivity analysis, interdisciplinary thinking, and understanding the value chain. In this approach, the instructor raises applied research questions and problems while presenting the course curriculum in association with broader engineering and technology value chain. In this paper, first the importance of a converging view in engineering education is presented. Then, by focusing on reservoir engineering course as part of the petroleum engineering curriculum, the technology chain of gas industry is described. Next, the link between topics of this course with such areas as chemical engineering, process design, is clarified. After that, real cases are presented to further discuss on the link between reservoir engineering and fluid flow hydrodynamics, carbonated reservoirs, engineering science history, intelligent techniques, and engineering mathematics. In each case study, the student becomes familiar with the output of research studies related to the subject and learns inherently the research methodology in engineering. The presented examples cover the reservoir engineering and value-chain topics from reservoir to refinery. The engineering education aligned with research empowers the student in integrated thinking, understanding different features of new reservoir engineering problems and reservoir economy.

Fault-karst reservoirs are featured by complex geological characteristics, and accurate and fast simulation of such kind of reservoirs using traditional simulator and simulation methods is pretty hard. Herein, we tried to discrete the complex fault-karst structures into one-dimensional connected units connecting the well, fracture and cave based on reservoir static physical parameters and injection-production dynamics. Two characteristic parameters, conductivity and connected volume, are proposed to characterize the inter-well connectivity and material basis. Meanwhile, the high-speed non-Darcy seepage term is introduced into the material balance equations for well-fracture-cave connected units to describe the actual seepage characteristics within the fault-karst reservoirs, and to better simulate the oil/water production dynamics. Based on this method, a fracture reservoir model of 1 injection-3 production was established. The change of oil–water action law in different injection and extraction systems under two production regimes of fixed production rate and fixed pressure is analyzed. A case study was conducted on S fault zone, where the flow of oil and gas did not follow the Darcy seepage rule and with a β value of 103–104, the single well flow pressure and oil production were perfectly matched with the real data. In addition, connected units with more prominent high-speed non-Darcy features were found to have better connectivity, which might shed light on the more accurate prediction of inter-well connectivity. Moreover, an improved injection-production well pattern and was proposed based on connectivity prediction model and reservoir engineering method to solve the problems of insufficient natural energy supply and overhigh oil production rate in Block S. Furthermore, the injection/production rate as well as the timing and cycle of water injection was predicted and optimized so as to better guide to site operations. [ABSTRACT FROM AUTHOR]

Low salinity waterflooding has gained significant attention and importance in the last decade, as it is seen as an impactful method for recovery of additional oil from carbonate reservoirs. Existing literature does not do justice to the underlying mechanisms that aid in the recovery of additional oil from such rock types. In this paper, we present a comprehensive review of the research conducted on low salinity waterflooding in carbonates and further provide a detailed and critical analysis on the same. The intention of this paper is also to present a condensed research summary on the said topic, and to chart out a detailed roadmap for future work, thereby opening the possibilities of new avenues of research in the field. [ABSTRACT FROM AUTHOR]

Aiming at the actual demands of petroleum exploration and development, this paper describes the research progress and application of artificial intelligence (AI) in petroleum exploration and development, and discusses the applications and development directions of AI in the future. Machine learning has been preliminarily applied in lithology identification, logging curve reconstruction, reservoir parameter estimation, and other logging processing and interpretation, exhibiting great potential. Computer vision is effective in picking of seismic first breaks, fault identification, and other seismic processing and interpretation. Deep learning and optimization technology have been applied to reservoir engineering, and realized the real-time optimization of waterflooding development and prediction of oil and gas production. The application of data mining in drilling, completion, and surface facility engineering etc. has resulted in intelligent equipment and integrated software. The potential development directions of artificial intelligence in petroleum exploration and development are intelligent production equipment, automatic processing and interpretation, and professional software platform. The highlights of development will be digital basins, fast intelligent imaging logging tools, intelligent seismic nodal acquisition systems, intelligent rotary-steering drilling, intelligent fracturing technology and equipment, real-time monitoring and control of zonal injection and production.

This research was conducted to determine how the incorporation of different poroelastic equations would affect the measured rock matrix bulk modulus in the laboratory. To do this, three experimental methods were used to measure the matrix bulk modulus, Ks, of seven sandstone specimens taken from the Świętokrzyskie mine in Poland. Those experimental methods were based on the different governing equations in poroelasticty theory. The matrix bulk modulus has a substantial impact on the rock strength against external stresses. Moreover, the rock bulk modulus depends directly on two components: the pore fluid bulk modulus and matrix bulk modulus. The second one is more important as it is much higher than the first one. In this study, the accuracy of those three methods in the measurement of the matrix bulk modulus was evaluated. For this purpose, an acoustic wave propagation apparatus was used to perform the required tests. For each method, an empirical correlation was extracted between the matrix bulk modulus and the applied hydrostatic stress. In all the experiments, an exponential correlation was observed between the matrix bulk modulus and the hydrostatic stress applied on the rock. Furthermore, it was found that the incorporation of the dry bulk modulus in the calculations led to an underestimation of the matrix bulk modulus. In addition, as the hydrostatic stress was raised, the matrix bulk modulus also increased. The applied methodology can be deployed to determine the matrix bulk modulus in coupled rock-fluid problems such as reservoir depletion, hydraulic fracturing, oil recovery enhancement, underground gas storage and land subsidence.

Compositional reservoir simulation is an important tool to model fluid flow in oil and gas reservoirs. Important investment decisions regarding oil recovery methods are based on simulation results, where hundred or even thousand of different runs are performed. In this work, a new methodology using artificial intelligence to learn the thermodynamic equilibrium is proposed. This algorithm is used to replace the classical equilibrium workflow in reservoir simulation. The new method avoids the stability test for single-phase cells in most cases and provides an accurate two-phase flash initial estimate. The classical and the new workflow are compared for a gas-oil mixing case, showing a simulation time speed-up of approximately 50%. The new method can be used in compositional reservoir simulations. [ABSTRACT FROM AUTHOR]

At present, the oil reservoir in Xishanyao Formation in Well Block Cai 9, Cainan Oilfield is high in water cut and low in production, with a well development rate of only 23.0%, and it will be abandoned and shut down. In order to improve the productivity and reduce the water cut of the ultra-high water-cut reservoir in this well block, analogy method, reservoir engineering and numerical simulation method were employed to analyze the feasibility of CO2 miscible displacement, develop reservoir engineering design, and perform pilot test. The results showed that Well Block C2576 in inverted seven-spot pattern with small spacing was selected as the pilot test area, Sublayer J2x12-2 rich in remaining oil was selected as the injection layer, the reasonable injection rate was 0.3 times the pore volume (the CO2 injection rate on ground was 10 500 t ), the gas injection velocity was 30-40 t/d, and the injection-production ratio was 1. 1-1. 2. The field test proved that after 4 529. 7 t CO2 injected cumulatively into this well block, the cumulative oil increase reached 1 269 t, and the oil displacement efficiency was 0. 27 t/t after CO2 injection, achieving a good stimulation effect. This research provides technical support for the pilot test, overall adjustment and development of CO2 miscible displacement in Cainan Oilfield. [ABSTRACT FROM AUTHOR]