Reservoir Simulation Model Research Articles

Underpinnings of reservoir and techno-economic analysis for Himalayan and Son-Narmada-Tapti geothermal sites of India

The high capital cost and risks associated with the extraction process of geothermal energy are key factors in the project economics. This paper presents a comprehensive technoeconomic study based on reservoir heat and flow simulations along with the levelized cost of energy (LCOE) for two geothermal sites (Himalayan and Son-Narmada-Tapti i.e. SONATA) in India.Dual porosity reservoir simulation models were constructed and were used to obtain the cumulative and the dynamic thermal energy potentials of these sites. The simulation studies suggest that both sites are equally potent for geothermal energy extraction. Comparatively, the proven cumulative energy potentials for the Himalayan and SONATA sites are found to be 4.92–5.10 and 3.62–3.7 TW h, respectively, for the 30 years of production at a rate of 92 kg/s. This difference can be attributed mainly to higher temperatures and deeper sites at the Himalayan Province. Sensitivity analyses were conducted to assess the impact of various parameters, including porosity, permeability, fracture spacing, and thermal conductivity. The results suggest that porosity and permeability are the key enablers of efficient energy production. The economic feasibility study suggests that the LCOE costs are high for these sites ($148/MWh for the Himalayan and $137/MWh for the SONATA). These two sites have CO2 mitigation potential in the range of 3.12 and 11.36 Megatons from a single doublet well configuration considering the CO2 emissions from conventional coal-fired thermal power plants. Overall, SONATA can be the better prospect considering the logistics and operational challenges along with reservoir heat potential.

Techno-economic-environmental study of CO2 and aqueous formate solution injection for geologic carbon storage and enhanced oil recovery

As carbon capture, utilization, and storage (CCUS), carbon-dioxide enhanced oil recovery (CO2 EOR) has inherent shortcomings, such as inefficient oil recovery and carbon storage, and low storage security with mobile CO2. This paper presents a techno-economic-environmental analysis of using formate species, a product of CO2 electrochemical reduction, as an alternative carbon carrier for sequestration and EOR in a carbonate oil reservoir in the Gulf of Mexico Basin. CO2 injection, water-alternating-CO2 injection, and aqueous formate solution injection were compared using a compositional reservoir simulation model and an economic calculator. Formate solution injection yielded greater levels of oil recovery and net carbon storage, where the carbon-bearing species resided in the dense aqueous phase without having to rely on petrophysical trapping mechanisms (structural and capillary). The enhanced oil production, net carbon storage, and storage security can be promoted by providing formate-based CCUS with more incentives (e.g., greater tax credit) in comparison to CO2-based CCUS for EOR and the manufacture of chemicals and products. In establishing carbon storage incentive policies and regulations, policymakers should include alternative carbon carriers.

From single to multi‐purpose reservoir: A framework for optimizing reservoir efficiency

AbstractThe prevailing challenge centers on the limited application of single‐purpose flood control reservoirs transitioning to multi‐purpose reservoirs, which, despite their potential, often fall short in addressing the escalating demands for diverse water resource management. These challenges are compounded by outdated operational rules and changing environmental conditions. This research develops a framework to enhance the dual functionality of reservoirs, initially designed for flood control, to also support water supply through the determination of maximum safe water levels (MSWLs). Utilizing historical inflow data and reservoir simulation models, the study identifies opportunities for optimizing United States Army Corps of Engineers Louisville District reservoirs. It highlights certain reservoirs as ideal for augmenting water supply capabilities without compromising flood control performance. Others remain critical for flood management due to limited water supply potential, underscoring the importance of maintaining a focus on flood control. The findings illuminate the intricate balance required between managing flood risks and enhancing water supply, indicating that precise operational adjustments can significantly improve reservoir sustainability and efficiency. This method offers a viable pathway to convert single purpose reservoirs into multi‐purpose reservoirs, meeting growing water demands while ensuring robust flood mitigation, and making a step toward better water utilization.

Hybrid machine learning framework for multi-well trajectory optimization in an unconventional field

The well placement problem in the petroleum industry is usually solved by integrating an optimizer with a reservoir simulation model. This is a time-consuming approach requiring thousands of simulation runs depending on the complexity of the reservoir system. This paper introduces two well placement optimization models: a Fast-Marching Model (FMM) based on fluid flow physics and a Hybrid Model integrating reservoir simulations and FMM results with a gradient boosting algorithm. These models prioritize speed and accuracy when integrated with genetic algorithm optimization, demonstrated using a synthetic unconventional field. From 290 randomly selected locations across the synthetic unconventional field, simulation runs were performed to determine cumulative gas production and used as ground truth. Using reservoir properties at these locations as inputs, Relative Opportunity Ranking (ROR) maps were generated using the FMM and Hybrid Model approaches. The ROR maps were then linked with a genetic algorithm to determine optimal well locations, iteratively updating ROR maps with penalty maps until all wells were appropriately placed. Results indicated that the standalone FMM generated ROR maps were highly correlated with the simulation results. Time complexity analysis revealed that both models were significantly faster than traditional methods, with the FMM independent of simulation and the Hybrid Model requiring only about 290 simulation runs. Ultimately, these models have shown tremendous potential for integration into current reservoir engineering workflows, reducing decision-making times in both greenfield and brownfield scenarios.

APPROACH TO CREATION OF GEOLOGICAL AND RESERVOIR SIMULATION MODEL AND METHODS FOR HISTORY MATCHING OF RESERVOIR ENERGY STATE

Under conditions of complex geological structure and substantial share of hard-to-recover oil reserves, reservoir simulation is a key tool in reservoir management. Direct problems solved by reservoir simulation include forecasting of the production performance, control and management of reservoir development, and localization of residual oil reserves. Inverse problem solution is useful for updating the knowledge of geological structure, reservoir quality, and historical production data by means of history matching. The quality of history matching is fundamental to ensure reliable future forecasts and localization of residual oil zones. The main approaches to appropriate history matching rely on analysis and adjustment of well logging and geological parameters, as well as checking and correction of errors, inadequate historical data, application of physics-based and reasonable approaches and tools. The prerequisite for adequate history matching and predictive ability of the resultant reservoir simulation model is the compliance of simulated and actual energy state of the reservoir. In this paper, the initial stage of reservoir simulation is performed, the approach to history matching of reservoir energy state aimed at improving the quality of further predictions is described as exemplified by one of the production areas of Russian oil field. The main causes of poor history match of formation and bottomhole pressures are revealed. These causes are grouped as follows: — geological causes/reservoir quality, which include permeability field distribution, reservoir connectivity, and determination of rock and fluid compressibility;— technical causes (during reservoir simulation), that is model limitations in case of larger areas, the need to use sector models, consideration of buffer region;— technological causes, including inadequate historical data or lack of data, detection of behind-the-casing flows and production casing leaks in wellbore, etc. For each group of factors that have a substantial impact on reservoir pressure, recommendations and methods of energy state history matching on geological and reservoir simulation models are provided, examples are given.

Experimental investigation of capillary pressure diagram in oil/water transition zone of low-permeability reservoirs

The oil/water transition zone, particularly pronounced in low-permeability formations with small pore diameters and high capillary pressure, contains a substantial portion of original oil in place. The capillary pressure curves provide essential data for the calculation of the original oil in place as well as the oilfield development simulation. However, the traditional single capillary pressure curve cannot provide enough information. To address this challenge, an experimental investigation was conducted to explore the capillary pressure diagrams within the oil/water transition zone of low-permeability reservoirs. The results shed light on the complex behavior of capillary pressure, the influence of initial water saturation, and the relationship between initial oil saturation and residual oil saturation. Incorporating the behavior of capillary pressure into reservoir simulation models can enhance reservoir characterization and improve the accuracy of oil production performance in low-permeability reservoirs.

The geomechanical response of the Gulf of Mexico Green Canyon 955 reservoir to gas hydrate dissociation: A model based on sediment properties with and without gas hydrate

Gas hydrate is commonly found concentrated in sand- and silt-rich reservoirs and these are thought to have great potential as an energy resource. To evaluate this potential, it is critical to understand their in-situ stress state and their behavior during production. We explore the geomechanical behavior of sandy silt sediments from a hydrate reservoir at Green Canyon GC 955, deep-water Gulf of Mexico without hydrate and compare these results to previous work on the same sandy silty sediments with hydrate present. Previous results from hydrate-bearing sediments at GC 955 using intact specimens show that the ratio of lateral to axial effective stress under uniaxial strain (K0) is strongly time-dependent and over long timescales approaches 1 (isostatic stress state). New results from sediments without hydrate using reconstituted specimens reveal that K0 is 0.51 and independent of time and stress. The reservoir without hydrate is approximately three times more compressible than with hydrate. In addition, we find that hydrate-free sediments have a normalized creep rate of Cα/Cc of about 0.028 at all stress levels, whereas hydrate bearing sediments exhibit a decrease in Cα/Cc with stress. We use these observations to present a new geomechanical model that describes how stress and porosity evolve during production of the hydrate reservoir. The key insight in our proposed model is that the sediment skeleton and gas hydrate phases share any applied external load in proportion to their relative stiffnesses. The model results predict that hydrate-bearing sediments are stiffer than gas hydrate-free sediments and that K0 reduces dramatically after gas hydrate dissociation, as is observed. At GC 955, a 5 MPa depressurization is needed to cause gas hydrate dissociation. This change in pore pressure increases the vertical and horizontal effective stresses from 3.8 to 8.8 MPa and 3.8–4.4 MPa, respectively. The dramatic vertical effective stress increase will cause significant compaction (7%) and perturb stresses near the wellbore. These experimental observations and the load-sharing model presented have the potential to underpin a new generation of hydrate reservoir simulation models, which may drive more effective production approaches.

Enhancing economic sustainability in mature oil fields: Insights from the clustering approach to select candidate wells for extended shut-in

Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts. In periods of significant price drops, companies may consider extended duration of well shut-ins (i.e. temporarily stopping oil production) for economic reasons. For example, prices during the early days of the Covid-19 pandemic forced operators to consider shutting in all or some of their active wells. In the case of partial shut-in, selection of candidate wells may evolve as a challenging decision problem considering the uncertainties involved. In this study, a mature oil field with a long (50+ years) production history with 170+ wells is considered. Reservoirs with similar conditions face many challenges related to economic sustainability such as frequent maintenance requirements and low production rates. We aimed to solve this decision-making problem through unsupervised machine learning. Average reservoir characteristics at well locations, well production performance statistics and well locations are used as potential features that could characterize similarities and differences among wells. While reservoir characteristics are measured at well locations for the purpose of describing the subsurface reservoir, well performance consists of volumetric rates and pressures, which are frequently measured during oil production. After a multivariate data analysis that explored correlations among parameters, clustering algorithms were used to identify groups of wells that are similar with respect to aforementioned features. Using the field’s reservoir simulation model, scenarios of shutting in different groups of wells were simulated. Forecasted reservoir performance for three years was used for economic evaluation that assumed an oil price drop to $30/bbl for 6, 12 or 18 months. Results of economic analysis were analyzed to identify which group(s) of wells should have been shut-in by also considering the sensitivity to different price levels. It was observed that wells can be characterized in the 3-cluster case as low, medium and high performance wells. Analyzing the forecasting scenarios showed that shutting in all or high- and medium-performance wells altogether results in better economic outcomes. The results were most sensitive to the number of active wells and the oil price during the high-price period. This study demonstrated the effectiveness of unsupervised machine learning in well classification for operational decision making purposes. Operating companies may use this approach for improved decision making to select wells for extended shut-in during low oil-price periods. This approach would lead to cost savings especially in mature fields with low-profit margins.

Technology Focus: Reservoir Simulation (July 2024)

The early days of artificial intelligence (AI) and machine learning (ML) were filled with promise, but now we’re witnessing a wave of practical applications transforming reservoir engineering. The integration of AI/ML with traditional workflows marks a turning point, unleashing the immense potential of these proven techniques to address our everyday challenges in reservoir simulation. These advancements lead to more-accurate models and faster simulation turnaround times, allowing us to model various scenarios more efficiently. These techniques enable us to understand and simulate reservoir behavior with greater fidelity and focus on the underlying physics. This deeper understanding translates to more-confident decisions regarding reservoir management and development, with a clearer picture of potential risks. Physics-informed machine learning emerges as a significant step forward, improving our understanding and providing better models while boosting runtimes, convergence, and overall performance. Paper IPTC 23730 highlights its application in tackling computationally intensive tasks such as critical temperature prediction with high accuracy, leading to significant speed-ups in simulations, particularly for complex compositional models undergoing miscible gas injection. Paper IPTC 23935 presents a fundamentally different approach to reservoir simulation studies. Adaptive models use multiple smaller, specialized models instead of one giant model. These models can be developed and run independently, allowing for parallel workflows and significantly reduced turnaround times. Adaptive models foster a culture of rapid experimentation and iteration, aligning perfectly with the fail-fast approach—prioritizing the quick evaluation of ideas and discarding those that don’t show promise early on. Paper SPE 214855 provides a comprehensive overview of a complex problem—wellbore modeling. This paper offers practical insights directly relevant to my work. These advancements represent a significant leap forward, paving the way for more-powerful and -versatile reservoir models. As research and development continue, these techniques are poised to revolutionize how we manage and optimize oil and gas reservoirs. I hope you enjoy reading this selection of papers and find them enlightening. Recommended additional reading at OnePetro: www.onepetro.org. SPE 216722 Developing Consistent Relative Permeability and Capillary Pressure Models for Reservoir Simulation of Carbon Capture and Storage Projects by L.S. Lun, ExxonMobil, et al.

Bridging the gap: Integrating static and dynamic data for improved permeability modeling and super k zone detection in vuggy reservoirs

Permeability modeling in fractured and vuggy reservoirs presents significant challenges, especially in carbonate reservoirs like Brazil’s Barra Velha Formation. The interpretation of vug properties is often subject to misconceptions. It is common to mistakenly consider vugs as part of the matrix, attributing uniform porosity and permeability properties. Alternatively, they are sometimes regarded as fractures, attempting to replicate the influence on permeability enhancement. However, vugs exhibit distinct behavior compared to fractures. Their characteristics are contingent upon factors such as geometry, size, and their relationship with the matrix. Existing methods often overlook the substantial impact of vuggy porosity, leading to inadequate representation of permeability values obtained from core plug measurements or downhole static data, such as Nuclear Magnetic Resonance (NMR) logs. In contrast, dynamic data derived from drill-stem tests (DSTs) and inferred from the Productivity Index (J) provide a more holistic view of the reservoir’s permeability, yet typically yielding a single permeability value for each interpreted interval. This research introduces a novel methodology that addresses these challenges by integrating static and dynamic data, with a primary focus on vuggy porosity using a permeability indicator from a dynamic source. A key innovation lies in the development of a method for estimating total permeability in vuggy intervals, accounting for the effects of vugs and calibrating permeability with dynamic data such as J, DST and Production Logging Tests (PLT). We substantiated the impact of vugs on the augmentation of permeability and production through the application of a localized reservoir simulation model around a key well. Two sensitivity analyses were conducted, one employing a grid representing matrix permeability and another incorporating total permeability with adjustments accounting for the influence of vugs. Additionally, we present two approaches for estimating vuggy porosity: one based on acoustic WellBore Image (WBI) and the second through image segmentation, which can be applied universally across different wellbore image formats. Furthermore, this study explores the concept of superkzones, representing intervals with exceptionally high permeability. The partitioning coefficient (CP), quantifying the relationship between vuggy porosity and total porosity, is employed to generate a probability curve for the occurrence of superkzones in intervals lacking production data. This curve accounts for critical factors such as pressure drop and fluid viscosity, providing a more comprehensive understanding of reservoir behavior. Through a comprehensive case study, our research aims to shed light on the intricate permeability characteristics of the Barra Velha Formation, while identifying the presence of superkzones. The proposed methodologies offer a bridge between static and dynamic data, resulting in a continuous permeability curve that aligns more closely with production data. Ultimately, this work contributes valuable insights for reservoir management and the production strategies optimization in complex carbonate reservoirs within the Santos Basin.

Forecasting the efficiency of waterflooding, thermal and chemical enhanced oil recovery methods in Bobrikovian reservoirs

The development of reserves contained viscous and highly viscous oil poses one of the major challenges in modern reservoir engineering. The poor performance of conventional waterflooding prompts the application of enhanced oil recovery (EOR) methods, of which thermal and chemical EOR are considered the most efficient. Reservoir simulation is a powerful tool for evaluating EOR performance. Serial runs of a history-matched model of a Bobrikovian reservoir are used to evaluate the incremental oil production due to each EOR technique and formulate the efficiency limits of the process performance criteria. In contrast to previous studies that evaluated EOR applications using reservoir simulation modeling, an entirely new quality model is derived through automatic history matching performed by solving an inverse problem. Another novel feature is the automated selection of the optimum field development scenario given a user-defined objective function. Evaluating the performance of hot water injection in a Bobrikovian reservoir suggests that this technology yields positive effects when the injected water temperature ranges from 50 °C to 90 °C. The surfactant–polymer flooding method is also investigated. The largest incremental oil production with a constant surfactant concentration of 0.5% is observed for a polyacrylamide concentration of 0.25%, which can be attributed to a water cut reduction due to plugging the well-drained and flushed reservoir zones. The greatest incremental oil production is achieved when injecting 1.5% surfactant and 0.25% polyacrylamide. The highest efficiency per ton of injected chemicals is obtained when injecting 0.5% polyacrylamide and 0.10% surfactant. Keywords: High-viscosity oil; EOR; thermal methods; surfactant-polymer flooding; reservoir simulation; history matching; injection simulation scenario.

Development of a New Correlation for Predicting Initial Water Saturation in Carbonate Reservoirs

The Middle East, rich in oil and gas within carbonate rocks, accounts for a significant portion of global reserves, drawing extensive exploration by major oil firms. Unlike Southeast Asia's fracture and cavity-dominated carbonate reservoirs, the Middle East features thick-bedded, pore-structured reservoirs with vast reserves. These complex and varied pore structures cause reservoir inhomogeneity, challenging the technical evaluation of these unconventional reservoirs. Characterization of carbonate reservoirs differs in terms of their mineralogical compositions and heterogenous pore systems from that of clastic reservoirs. Reservoir characterization seeks to build geological and petrophysical models for reservoir simulation. Rock types represent the most crucial characteristics of reservoirs for specialized facies modelling within specific ranges of porosity and permeability. Rock typing is an essential method routinely used by petroleum engineers for characterizing and predicting the reservoir quality of carbonate reservoirs by classifying reservoir rocks into distinct units based on similar petrophysical properties. It is imperative to predict these reservoir properties accurately and precisely. The J-function technique is considered the most effective rock typing procedure. In this study, a new correlation for predicting initial water saturation (Swi) for a reservoir producing from a Permian carbonate formation, located in the Arabian Peninsula, has been developed. The new empirical equation is an augmented Lucia model that utilizes capillary pressure (P�), porosity (), and permeability (k), as independent variables. The coefficient of multiple R2 , the student’s t and F-tests p-value were used in the model evaluation. R2 for the new model was about 0.92, t-test and F-test p-values were much lower than 0.05, indicating that the independent variables are significant. The model was also tested against an independent data set and yielded an R2 of 0.88. Likewise, the new correlation was compared to Lucia’s model and showed better results. The goal of the study is to use the developed correlation in the geostatistical modeling of connate water saturation for analogous formations in the region.

Hybrid Approach Proves Effective in Multiwell Forecasting in Unconventionals

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3855422, “RGNet for Multiwell Forecasting in Unconventional Reservoirs,” by Zhenyu Guo, SPE, and Sathish Sankaran, SPE, Xecta Digital Labs, and Ying Li, The University of Tulsa. The paper has not been peer reviewed. _ The aim of the complete paper is to propose a hybrid approach that combines physics with data-driven approaches for efficient and accurate forecasting of the performance of unconventional wells under codevelopment. The method the authors propose is the reservoir graph network (RGNet) model. By reducing system complexity while maintaining fundamental physics, the model provides an efficient and accurate way to model, history-match, and predict unconventional wells. Compared with a full-physics model that takes from hours to days to run, the described model only takes from seconds to minutes. Performance Analysis of Unconventional Reservoirs Developing unconventional reservoirs is a complex process involving well targeting, timing, spacing, and completion design for horizontal wells with hydraulic fractures. Among commonly used methods, decline curve analysis (DCA) can execute quick well-performance analysis without considering the complex physics in unconventional reservoirs piece by piece. The convenient aspect of DCA is that practitioners can obtain forecast results quickly by providing historical production data. However, because DCA has some strong assumptions on operational conditions and does not incorporate the key physics of the flow, it may fail to predict accurately in many situations. As a simple, fast tool for analyzing the capacity of a reservoir, rate transient analysis (RTA) has been used to model and forecast unconventional wells. Both pressure and rate data are considered to generate solutions that are more credible than those generated by DCA, where only rate information is used. The drawback of RTA is related to significant assumptions used to derive analytical solutions (e.g., homogeneous reservoir and simple planar fractures with uniform properties). Moreover, it is not an easy task to acquire some of the information required by RTA. Full-physics reservoir simulation models are used as the most-rigorous approach to understand dynamics of unconventional reservoirs because they allow for different complexities for modeling unconventionals. However, because it takes tremendous effort for information gathering, geological/fracture modeling, and history matching, applying this approach to large-scale reservoirs is not tractable. Based on the idea of diffusive time of flight (DTOF), the RGNet model was developed for reservoir modeling, history matching, forecasting, and optimization. It can model multiple wells with communications. In this work, the authors incorporate pressure dependency of pore volumes and transmissibility into RGNet while modeling unconventional reservoirs. Methodology The basic idea of the described model comes from the DTOF transformation that converts the 3D diffusivity problem into a 1D problem. The complete paper provides several equations that enable this conversion.

Investigation on the Extent of Retrograde Condensation of Qianshao Gas Condensate Reservoir Using PVT Experiments and Compositional Reservoir Simulation

In the development of the Qianshao (QS) gas condensate reservoir, it is crucial to consider the phenomenon of retrograde condensation. Understanding the condensate saturation distribution with respect to time and space within the reservoir is essential for planning and implementing effective strategies for the future development of the QS gas condensate reservoir. In this paper, various PVT experiments (including reservoir oil recombination, flash separation, constant composition expansion, and constant volume depletion) were conducted to study the PVT properties and phase behavior of QS gas condensate fluid. Based on experimental data, our in-house PVT computation package was used to determine the appropriate EOS model parameters for the QS gas condensate. A four-step reservoir fluid characterization procedure and workflow for gas condensate reservoirs was developed. Furthermore, by analyzing the pressure-temperature phase envelope, the maximum possible condensate saturation in the QS well area was estimated to be around 3%. Numerical reservoir simulation models were developed using both the EOS model and actual reservoir engineering data. These simulation models were specifically designed to replicate the retrograde condensation process that occurs during production, taking into account both vertical and horizontal wells. By simulating the production process, these single-well reservoir simulation models enable us to quantitatively evaluate the condensate saturation and its distribution over space and time within a specific control area around a single well. Reservoir simulation results show that the condensate build-up around vertical and horizontal wells is quite different. For a vertical well, the maximum condensate oil saturation (30%) around the wellbore is located approximately 5 to 6 m from the well’s center. In contrast, the horizontal well model demonstrates a maximum condensate saturation of no more than 1.5%. This information is crucial for making informed decisions regarding the effective development and management of the QS gas condensate reservoir.

Efficient AI-Physics hybrid model with productive capabilities to reduce the time of history matching and scenario assessment; a case study: Minagish oil field

This paper proposes a novel approach that combines physics-based numerical simulation with deep-learning neural networks to create an AI-Physics hybrid model for reservoir simulation. Our primary objective is to reduce the time of history match and achieve the best match between model and observation data using our hybrid model. We trained our model using historical data and created a reliable forecasting model that predicts field behavior and proposes new scenarios to improve production in the upcoming years. To test our model, we combined AI physics history training with blind test prediction calculations of the remaining oil map, and the AI physics model created a forecast scenario definition based on this map. Our proposed simulation method can reduce the time of history matching and scenario evaluation by 90-95%. We created three improved forecast scenarios based on predefined scenarios that can produce millions of standard barrels more oil over three years than the original development scenario. The results demonstrate the potential of our AI-Physics hybrid model in revolutionizing reservoir simulation in the oil and gas industry. Our approach can significantly reduce simulation time, improve forecasting accuracy, and optimize production strategies, leading to significant economic benefits.

Automatic History Matching for Adjusting Permeability Field of Fractured Basement Reservoir Simulation Model Using Seismic, Well Log, and Production Data

Developing automatic history matching (AHM) methods to replace the traditional manual history matching (MHM) approach in adjusting the permeability distribution of the reservoir simulation model has been studied by many authors. Because permeability values need to be evaluated at hundreds of thousands of grid cells in a typical reservoir simulation model, it is necessary to apply a reparameterization technique to allow the optimization algorithms to be implemented with fewer variables. In basic reparameterization techniques including zonation and pilot point methods, the calibrations are usually based solely on the production data with no systematic link to the geological and geophysical data, and therefore, the obtained permeability distribution may be not geologically consistent. Several other reparameterization techniques have attempted to preserve geological consistency by incorporating 4D seismic data; however, these techniques cannot be applied to our fractured basement reservoirs (FBRs) as they do not have 4D seismic data. Taking into account these challenges, in this study, an AHM methodology and workflow have been developed using a new reparameterization technique. This approach attempts to minimize the potential for geological nonconsistency of the calibrated results by linking the permeability to geophysical data. The proposed methodology can be applied to fields with only traditional geophysical data (3D seismic and conventional well logs). In the proposed workflow, the spatial distributions of seismic attributes and geomechanical properties were calculated and estimated from 3D seismic data and well logs, respectively. After that, a feed-forward artificial neural network (ANN) model trained by the back-propagation algorithm of the relationship between initial permeability with seismic attributes and geomechanical properties of their grid cell values is developed. Then, the calibration of the permeability distribution is performed by adjustment of the ANN model. Modification of the ANN model is performed using the simultaneous perturbation stochastic approximation (SPSA) algorithm to calibrate transmission coefficients in the ANN model to minimize the discrepancy between the simulated results and observed data. The developed methodology is applied to calibrate the permeability distribution of a simulation model of Bach Ho FBR in Vietnam. The effectiveness of the methodology is evident by comparing the historical matches with an available manually history-matched simulation model. The application shows that the proposed methodology could be considered as a suitable practical approach for adjusting the permeability distribution for FBR reservoir simulation models.

Work Flow Screens Surfactants for EOR Through Wettability Alteration

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3866246, “A Systematic Laboratory and Model-Based Surfactant Screening Work Flow for Enhanced Recovery Through Wettability Alteration in the Eagle Ford,” by Jason Jweda, SPE, Russ Bone, SPE, and Hesham El-Sobky, ConocoPhillips, et al. The paper has not been peer reviewed. _ Previous experimental studies and published field trials have shown that injected surfactants can be an effective means of improving recovery. Most experiments, however, have been conducted at ambient rather than reservoir conditions. The complete paper presents a novel approach to screen thermally stable surfactants at high pressures and high temperatures for the explicit purpose of wettability alteration in the operator’s Eagle Ford acreage. Early Internal Efforts Surfactant usage was initiated at the inception of the play. The use of these surfactants was discontinued after approximately 5 years of field development. Fieldwide performance analysis confirmed that off-the-shelf surfactants, on average, did not improve long-term performance statistically. Initial laboratory screening of bespoke surfactant chemistries commenced a few years later. To test surfactant properties near Eagle Ford reservoir conditions, a novel microfluidics experiment was conducted using synthetic microchips designed with reservoir specifications. Twenty-six flowback tests with Eagle Ford crude oil were conducted at 300°F and up to 3,800 psi with six different commercial surfactant chemistries. Imbibition results with the six commercial surfactants were compared with a control case with make-up brine water. Testing performed at 2 gal/thousand increased overall performance for the oil-wet conditions but decreased performance for mixed-wet conditions when compared with 1 gal/thousand. The surfactants tested in this series were temperature-dependent and had clogging tendencies of varying degrees within the microfluidics chips. Methods and Work Flow Promising results from the Amott cell imbibition and other experiments encouraged the authors to evaluate surfactants more holistically, with an emphasis on testing at Eagle Ford reservoir conditions. The first set of experiments involved fluid-stability tests, contact-angle measurements, interfacial-tension (IFT) measurements, static spontaneous-imbibition experiments, and completion-fluid-compatibility tests. An extensive set of commercially available surfactants was screened. The highest potential cationic and nonionic cosurfactant blends that passed through fluid stability, contact-angle, and static spontaneous-imbibition experiments were selected for hydraulic stimulation-fluid compatibility tests. After the compatibility tests, the two best-graded cationic and nonionic cosurfactant blends were used in a novel flowback experiment coupled with multistage nuclear magnetic resonance (NMR) measurements. Mechanistic reservoir simulation models were used to evaluate the production effect of wettability change caused by surfactant injection. The combination of laboratory-based experimentation and reservoir simulations provided strong confidence in holistically evaluating the potential of cosurfactant blends in the Eagle Ford.

A New Gradient-Accelerated Two-Stage Multiobjective Optimization Method for CO2-Alternating-Water Injection in an Oil Reservoir

Summary CO2-water-alternating-gas (CO2-WAG) is a reservoir development method that can simultaneously enhance oil recovery and achieve CO2 storage. However, improperly designed parameters for CO2 injection and oil production may significantly reduce the oil displacement efficiency and CO2 storage. Furthermore, optimizing the injection parameters is computationally expensive due to the high computational cost of the compositional simulation. This work aims to propose an efficient optimization method to obtain a series of well-control schemes that balance maximizing net present value (NPV) and CO2 storage for decision-makers. Given the number of CO2-WAG cycles and the duration of each cycle, we optimize the water injection rate, gas injection rate, and half-cycle for the injection well and the bottomhole pressure (BHP) for the production well. In this paper, a two-stage optimization strategy is proposed to enhance the optimization efficiency. The first stage performs the surrogate-assisted single-objective optimizations of each considered objective. It is designed to find the endpoints of the Pareto front that connect all solutions of the multiobjective optimization; this stage not only provides important search directions for the subsequent multiobjective optimization but also improves the accuracy of the surrogate model near the Pareto front. The second stage is the surrogate-assisted multiobjective optimization, which aims to find all the solutions along the Pareto front based on the Pareto endpoints obtained from the first stage. In addition, this study successfully combines the gradient of the objective functions with the meta-heuristic algorithm during the multiobjective optimization, which ensures a faster convergence to the global optimum. The proposed multiobjective optimization algorithm shows faster convergence than the conventional optimization methods when applied to the three multiobjective optimization test functions. Finally, a comparison with the conventional multiobjective optimization is conducted based on one test function and two benchmark reservoir simulation models to verify the correctness and efficiency of the proposed method. It is confirmed that the proposed method outperforms the conventional ones for the optimization of CO2-WAG injection.

Flow Profile Estimating in production wells based on chemical composition of fluids (an example on Volga-Ural Petroleum and Gas Province)

Current problems in mature oil fields are high water cut and flow profile estimating of oil and associated brines from different layers. To establish the flow profile in production wells, geophysical research (Production Logging) is traditionally used by lowering special equipment into the well. Production Logging requires production stops and labor costs. Geochemical methods (Production Geochemistry) are used as an alternative solution: sampling is simple and efficient, which makes it possible to cover all the interesting area. Moreover, sampling does not require stopping the well. The geochemical method uses individual indicators of the composition of formation fluids produced from different perforation intervals. In this work, geochemical studies were carried out using wellhead samples from more than 100 wells, with single perforation for carbonate and terrigenous reservoirs. Some wells have joint exploitation of these formations. An automated algorithm was used to identify the distinctive characteristics of each formation based on the composition of the produced brines and oils. Data on the chemical composition of fluids from different development objects made it possible to determine the flow profiles in wells with joint production. Based on the results of the studies, the Devonian reservoir of the field under consideration is divided into 2 parts – northern and southern, which differ in the chemical composition of formation fluids. The same separation of the deposits into 2 parts is noted by field development analysis: over the past 50 years, the main production of oil and associated brines has been concentrated in the southern part of the deposit, confined to the fault, where the active work of the aquifer is assumed. It is recommended to use the obtained data for history matching of the reservoir simulation model.

Construction of adaptive reduced-order reservoir models based on POD‑DEIM approach

This paper introduces a method for constructing adaptive reduced-order reservoir simulation models based on the POD-DEIM approach for field development optimization and assisted history matching problems. The approach is based on adapting the orthogonal decompositions bases to the varying model configuration. The method utilizes information contained in the bases of the original model and supplements them with new components instead of constructing a new model from scratch. Adapting the bases significantly reduces the computational costs of building reduced-order models and allows the application of such models to tasks requiring multiple simulations with different configurations. The paper presents an implementation of the POD-DEIM model for a two-phase flow problem and discusses examples of adapting this model to changes in well configuration and geological properties of the reservoir. We propose a generalized approach using POD-DEIM models in combination with the bases adaptation technique to solve optimization problems, such as field development optimization, selection of the optimal well locations, geometries, and well regimes, as well as history matching.