Reservoir Simulation Model Research Articles

Simulation-based evaluation of the effectiveness of fiber-optic sensing in monitoring and optimizing water circulation in next-generation enhanced geothermal systems

The success of multi-stage hydraulic fracturing technology in unconventional oil and gas reservoir development has attracted much attention from the geothermal community. Some pilot field experiments have been conducted to investigate the multi-stage hydraulic stimulation along horizontal or high-deviated wellbores in geothermal reservoirs (denoted as next-generation enhanced geothermal systems). One common output of such stimulation is that the created hydraulic fractures show various conductive capabilities, which could potentially lead to undesirable flow localization, early thermal breakthrough, and low cold water sweep efficiency. Monitoring the water circulation between horizontal wellbores connected through hydraulic fractures becomes important in avoiding early severe producing temperature reduction in practical operations. In this study, we investigate the effectiveness of distributed temperature sensing (DTS) and distributed strain sensing (DSS) in identifying dominant flow paths in geothermal reservoirs with multi-stage hydraulic stimulation and horizontal well completion designs. A coupled thermo-poro-elastic model for general-purpose geothermal reservoir simulation is developed and solved using a combined finite volume/finite element method, together with a fully implicit sequential coupling scheme. The temperature and strain responses along horizontal production wells are simulated and analyzed during water circulation. Reservoir temperature reduces in the area that is close to the water flow paths, which induces thermal contraction. These thermoelastic characteristics can be monitored by fiber-optic sensing, forming distinct signatures for flow-localization identification. DTS data may be dominantly influenced by the fractures with higher conductivity, ‘masking’ the signals related to fractures in the downstream, but DSS data can provide additional information for accurate identification of high conductive fractures. Successful mitigation strategies, for example plugging the dominant fractures illustrated in this study, could substantially increase the producing fluid temperature. The analyses in this study show that fiber-optic sensing is an effective technology to monitor and optimize water circulation in next-generation enhanced geothermal systems.

Optimal Choices in Decision Supporting System for Network Reservoir Operation

The aim of this research was to identify optimal choices in decision support systems for network reservoirs by using optimal rule curves under four scenarios related to water scarcity and overflow situations. These scenarios were normal water shortage, high water shortage, normal overflow and high overflow situations. The application of various optimization techniques, including Harris Hawks Optimization (HHO), Genetic Algorithm (GA), Wind-Driven Optimization (WDO) and the Marine Predator Algorithm (MPA), in conjunction with a reservoir simulation model, was conducted to produce alternative choices, leading to suitable decision-making options. The Bhumibol and Sirikit reservoirs, situated in Thailand, were selected as the case study for the network reservoir system. The objective functions for the search procedure were the minimal average water shortage per year, the minimal maximum water shortage and the minimal average water spill per year in relation to the main purpose of the reservoir system using the release criteria of the standard operating policy (SOP) and the hedging rule (HR). The best options of each scenario were chosen from 152 options of feasible solutions. The obtained results from the assessment of the effectiveness of alternative choices showed that the best option for normal water scarcity was the rule curve with the objective function of minimal average water shortage per year, using HR and recommended SOP for operation, whereas the best option for high-water shortage situation was the rule curves with objective function of minimal of maximum water shortage using HR and recommended HR for operation. For overflow situation, the best option for normal overflow situation was the rule curves with objective function of minimal average water spill per year using HR and the recommended SOP for operation, whereas the best option for the high overflow situation was the rule curve with the objective function of minimal average water spill per year using HR and the recommended HR for operation. When using the best curves according to the situation, this would result in a minimum water shortage of 153.789 MCM/year, the lowest maximum water shortage of 1338.00 MCM/year, minimum overflow of 978.404 MCM/year and the lowest maximum overflow of 7214.00 MCM/year. Finally, the obtained findings from this study would offer reliability and resiliency information for decision making in reservoir operation for the multi-reservoir system in the upper region of Thailand.

Extracting Optimal Operation Rule Curves of Multi-Reservoir System Using Atom Search Optimization, Genetic Programming and Wind Driven Optimization

This research aims to apply optimization techniques using atom search optimization (ASO), genetic programming (GP), and wind-driven optimization (WDO) with a reservoir simulation model for searching optimal rule curves of a multi-reservoir system, using the objective function with the minimum average quantity of release excess water. The multi-reservoir system consisted of five reservoirs managed by a single reservoir that caused severe problems in Sakon Nakhon province, Thailand, which was hit by floods in 2017. These included Huai Nam Bo Reservoir, the Upper Huai Sai-1 Reservoir, the Upper Huai Sai-2 Reservoir, the Upper Huai Sai-3 Reservoir, and the Huai Sai Khamin Reservoir. In this study, the monthly reservoir rule curves, the average monthly inflow to the reservoirs during 2005–2020, the water demand of the reservoirs, hydrological data, and physical data of the reservoirs were considered. In addition, the performance of the newly obtained rule curves was evaluated by comparing the operation with a single reservoir and the operation with a multi-reservoir network. The results showed situations of water shortage and water in terms of frequency, duration, average water, and maximum water. The newly obtained rule curves from the multi-reservoir system case showed an average water excess of 43.722 MCM/year, which was less than the optimal curves from the single reservoir case, where the average water excess was 45.562 MCM/year. An analysis of the downstream reservoir of the multi-reservoir system, which diverts water from the upstream reservoirs, was performed. The results showed that the new optimal rule curves of ASO, GP, and WDO operated as a multi-reservoir system performed better than when operated as a single reservoir. Therefore, this research is suitable for sustainable water management without construction.

Produced-Water Salinity Data Optimizes Net Present Value

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200941, “Optimizing Oilfield Net Present Value With Produced-Water Salinities and Tracers,” by Babalola Daramola, SPE, Propellio. The paper has not been peer reviewed. _ The complete paper presents the value-adding applications of produced-water salinity data in three fields (B, C, and D) offshore Nigeria. The author presents case studies in which produced-water salinity data were used to transform the performance of oil-producing fields with platform well developments. Produced-water salinity data were used to improve Field B’s reservoir simulation history match, generate infill drilling targets, and reinstate Field C’s oil production. Water-Salinity Evaluation Work Flow The authors recommend the following work flow for collecting and evaluating produced-water salinity data: 1. Ensure that the oil-production asset has a production-test pipeline and a test separator 2. Divert the production well to the test separator and collect water samples at least once a month 3. Minimize contamination between water samples from different wells 4. Measure dissolved ions in each water sample 5. Estimate water salinity by summing up dissolved solids in milligrams per liter or parts per million 6. Convert water-salinity data units to parts per thousand 7. Create a water-salinity plot showing water-production rates (STB/D and water salinity). 8. Add the 100% formation water line to the water-salinity plot 9. Add the 100% seawater line to the water-salinity plot 10. Evaluate the water-salinity trend and differentiate formation water and injected seawater 11. Identify wells with barium sulfate (BaSO4) scaling potential 12. Recommend scale-remediation measures 13. Recommend remediation measures to remove scale 14. Use water-salinity data to track injected-seawater arrival times at production wells Field B Case Study: History Match and Waterflood Transformation Field B is a sandstone reservoir with undersaturated oil, four production wells (BP1, BP2, BP3, and BP4), and two water-injection wells (BW1 and BW2). Well BP1 achieved first oil production in June 1981; water breakthrough occurred in December 1983. A sliding-sleeve water-shutoff workover was executed in 1987. Well BP2 achieved first oil production in February 1984; water breakthrough occurred in August 1986. Well BP3 achieved first oil production in July 1984; water breakthrough occurred in March 1986. Although the field had two water injectors, the production-well liquid rates declined over the production period. A reservoir simulation model was constructed to evaluate the field’s performance and generate infill drilling opportunities. The asset team struggled to history match the water cuts in Wells BP1, BP2, and BP3 and were advised to deduce the water source in the wells and use this information to tune the reservoir simulation model. Well BP1’s produced water was mainly formation water between 1983 and 1993; injected seawater was not seen at this well. This, along with declining liquid rates, suggest that Well BP1 received little or no water injection support from Well BW1. This is most likely the result of the 2-km distance between Wells BW1 and BP1. Well BP2’s produced water was mainly formation water between 1984 and 1989; injected seawater was not seen at this well. This, along with declining liquid rates, suggest that Well BP2 received little or no water injection support from Well BW1, most likely the result of the 3-km distance and the partially sealing fault between Wells BW1 and BP2.

Factoring Permeability Anisotropy in Complex Carbonate Reservoirs in Selecting an Optimum Field Development Strategy

Current methods of oil and gas field development design rely on reservoir simulation modeling. A reservoir simulation model is a tool to reproduce field development processes and forecast production data. Reservoir permeability is one of the basic properties that determines fluid flow. From existing approaches, the porosity and permeability values should be consistent with petrophysical correlations obtained from core sample tests in the course of development of an absolute permeability cube in the reservoir simulation model. For carbonate reservoirs with complex pore space structure and fractures, the petrophysical correlations are often unstable. To factor in the fluid flow in a fractured rock system, dual-medium models are developed, allowing for matrix and fracture components. Yet in this case, the degree of uncertainty only increases with the introduction of a new parameter: a cross-flow index of fluid migration from matrix to fracture, which is only determined indirectly by results of fluid flow studies conducted in the initial development period, and therefore most often is adaptive. Clearly, for well-studied fields there is an extensive data pool drawn on research findings: core studies, well logging, well flow testing, flowmetry, special well-logging methods (FMI, Sonic Scanner, etc.); the dual-medium model development for such reservoirs is fairly well-founded and supported by actual studies. However, at the start of the field development, the data are incomplete, which renders qualitative dual-medium modeling impossible. This paper proposes an approach to factor in the target’s permeability anisotropy at an early development stage through the integration of well, core and 3D seismic surveys. The reservoir was classified into pore space types, to which different petrophysical correlations were assigned to develop a permeability array, and relative phase permeabilities were studied. The fluid flow model was history-matched with allowance for permeability anisotropy and rock types. Comparative calculations were conducted on the resulting model to select the optimum development strategy for the target.

Robust Method for Reservoir Simulation History Matching Using Bayesian Inversion and Long-Short-Term Memory Network-Based Proxy

Summary History matching is a critical process used for calibrating simulation models and assessing subsurface uncertainties. This common technique aims to align the reservoir models with the observed data. However, achieving this goal is often challenging due to the nonuniqueness of the solution, underlying subsurface uncertainties, and usually the high computational cost of simulations. The traditional approach is often based on trial and error, which is exhaustive and labor-intensive. Some analytical and numerical proxies combined with Monte Carlo simulations are used to reduce the computational time. However, these approaches suffer from low accuracy and may not fully capture subsurface uncertainties. This study proposes a new robust method using Bayesian Markov chain Monte Carlo (MCMC) to perform assisted history matching under uncertainties. We propose a novel three-step workflow that includes (1) multiresolution low-fidelity models to guarantee high-quality matching; (2) long-short-term memory (LSTM) network as a low-fidelity model to reproduce continuous time response based on the simulation model, combined with Bayesian optimization to obtain the optimum low-fidelity model; and (3) Bayesian MCMC runs to obtain the Bayesian inversion of the uncertainty parameters. We perform sensitivity analysis on the LSTM’s architecture, hyperparameters, training set, number of chains, and chain length to obtain the optimum setup for Bayesian-LSTM history matching. We also compare the performance of predicting the recovery factor (RF) using different surrogate methods, including polynomial chaos expansions (PCE), kriging, and support vector machines for regression (SVR). We demonstrate the proposed method using a water flooding problem for the upper Tarbert formation of the 10th SPE comparative model. This study case represents a highly heterogeneous nearshore environment. Results showed that the Bayesian-optimized LSTM has successfully captured the physics in the high-fidelity model. The Bayesian-LSTM MCMC produces an accurate prediction with narrow ranges of uncertainties. The posterior prediction through the high-fidelity model ensures the robustness and accuracy of the workflow. This approach provides an efficient and practical history-matching method for reservoir simulation and subsurface flow modeling with significant uncertainties.

An adaptive multi-objective reservoir operation scheme for improved supply-demand management

The complex hydrological connectivity of integrated river-reservoir catchment command regions in conjunction with abrupt societal development and population growth impose adverse water availability scenario in the global reservoirs. In this context, to formulate optimal reservoir operation policies, numerous reservoir simulation and optimization models are currently on board. However, lack of combined hydrologic and hydraulic conceptualizations in the existing reservoir operation framework fetches considerable uncertainty; thereby leading to formulation of erroneous reservoir operation rule. This study proposed an integrated Soil and Water Assessment Tool (SWAT) based hydrologic, HEC-ResSim based hydraulic, and genetic algorithm (GA) based optimization framework (SWAT-HEC-ResSim-GA) for formulating the optimal reservoir operation rule of the Kangsabati reservoir located in the eastern India. Unlike the existing reservoir operation rules, the present study incorporates a 10-daily-scale environmental flow estimation approach to quantify the water demand. The proposed reservoir operation framework generated rule curve based HEC-ResSim simulation revealed improved reservoir performance with reservoir time reliability, volume reliability, and resiliency magnitudes of 0.631, 0.736, and 0.591, respectively. Finally, this study endorses to use the developed methodology for supply–demand management, and subsequent, reservoir operation in any global reservoir catchment-command regions with utmost accuracy.

Life-Cycle Production Optimization of the CO2-Water-Alternating-Gas Injection Process Using Least-Squares Support-Vector Regression Proxy

SummaryIn this study, we present a framework for efficient estimation of the optimal carbon dioxide (CO2)-water-alternating-gas (WAG) parameters for robust production-optimization problems by replacing a high-fidelity model with a least-squares support-vector regression (LS-SVR) model. We provide insight and information on the proper selection of feature space and training samples of the LS-SVR proxy model for the CO2-WAG life cycle production optimization problem. Given a set of training points generated from high-fidelity model-based simulation results, an LS-SVR-based proxy model is built to approximate a reservoir-simulation model. The estimated optimal design parameters are then found by maximizing net present value (NPV) using the LS-SVR proxy as the forward model within an iterative-sampling-refinement (ISR) optimization algorithm that is designed specifically to promote the accuracy of the proxy model for robust production optimization. As an optimization tool, the sequential quadratic programming (SQP) method is used. CO2-WAG design variables are CO2 injection and water injection rates for each injection well at each cycle, production bottomhole pressure (BHP) for each production well at each WAG half-cycle, and inflow control valve (ICV) for each well at each WAG half-cycle and at each valve. We study different scenarios where we fix some of the design variables to investigate the importance of design variables on the life cycle production optimization of the CO2-WAG problem. We compare the performance of the proposed method using the LS-SVR runs with the popular stochastic simplex approximate gradient (StoSAG) and reservoir simulation runs for a synthetic example considering a three-layer, channelized reservoir with four injectors and nine producers. Results show that with the properly selected feature space and training points, the proposed LS-SVR-based ISR optimization framework is at least 1.5–8 times computationally more efficient, depending on the cases considered, than the StoSAG using a high-fidelity numerical simulator. However, we observe that the size and sampling of the training data, as well as the selection of well controls and their bound constraints for the well controls, seem to be influential on the performance of the LS-SVR-based optimization method. This is the first LS-SVR application to the CO2-WAG optimal well-control problem. The proposed LS-SVR-based ISR optimization framework has the potential to be used as an efficient tool for the CO2-WAG optimization problem.

Carbonate excess permeability in pressure transient analysis: A catalog of diagnostic signatures from the Brazil Pre-Salt

Dynamic appraisal in carbonates with excess permeability is critical to successful reservoir modeling and depletion planning. Accurate recognition and characterization of a dual-porosity system may translate to improved project performance. We present a catalog of diagnostic signatures that elevate pressure transient analysis beyond the traditional V shape, to aid in identifying non-matrix features’ presence, extent, and contribution to reservoir performance. We reviewed 152 well tests from the Brazil Pre-Salt, integrated with multi-scale static and dynamic data (conventional core, borehole image logs, seismic, and drilling losses). A recurring set of eight signatures with characteristic slopes and shapes stood out, which we reconciled with geologic concepts and tested with numerical modeling. These signatures reveal key insights into excess-permeability architecture and non-matrix types, from touching vugs to caves, from natural fractures to fault damage zones. They will assist subsurface teams to optimally frame well test objectives and maximize value of information during early appraisal and field development. They will help enhance reservoir performance prediction, by enabling a comprehensive use of well test data in geologic and reservoir simulation models.

Effect of CO2 tax on energy use in oil production: waterflooding optimization under different emission costs

Tackling emissions from hydrocarbon production is a necessity because hydrocarbon production will last for a prolonged time. As a popular hydrocarbon production method, waterflooding operation is energy-intensive and accounts for significant CO2 emissions. This article investigates the effect of CO2 tax level on recovery process of already producing fields with waterflooding. Our methodology is waterflooding optimization in reservoir simulation models, specifically optimizing well-controls. Unlike traditional studies, our optimization objective comprises two components: the profitability of hydrocarbon production and an additional tax proportional to CO2 emissions. The associated CO2 emissions is estimated using a scheme developed upon an integrated model of reservoir, surface network, and topside facility. We examine our methodology on two cases with heterogeneous reservoir models. In each case, we optimize multi-scenarios enforcing different CO2 tax rates. The solutions indicate that imposing a higher CO2 tax rate reduces both emissions and hydrocarbon production. The fractional reduction of oil produced is however smaller than the emission reduction. While an increased tax rate drives the topside equipment to operate at higher efficiencies, the main effects of a higher tax rate are reduced water injection and more efficient subsurface drainage. There is a non-linear relationship between the reduced production and emissions. For increasing tax levels there are diminishing returns on lower emissions, reflecting reduced opportunities for emission reduction by changes in the drainage strategy. Some increments on the tax rate will therefore have negligible impacts on the optimal drainage strategy, and hence an adverse effect on the profitability with negligible emission reduction.

Assessment of Activating Reservoir Emergency Storage in Climate-Change-Fueled Extreme Drought

With exacerbating climate change, the current reservoir storage capacity in South Korea is insufficient to meet the future scheduled water demand. No study has yet evaluated the effects of applying the water supply adjustment standard (Standard) and activating the reservoir emergency storage in response to extreme drought. The main objective is to assess the effects of applying Standard and activating emergency storage in meeting the water demand under extreme drought at six multipurpose reservoirs (Andong, Gimcheon-Buhang, Gunwi, Hapcheon, Imha, and Milyang) in the Nakdong River Basin, South Korea. We built a reservoir simulation model (HEC-ResSim), determined the extreme drought scenarios, and emergency storage capacity. We evaluated three reservoir operation cases (general operation, regular Standard, and revised Standard) from 2011 to 2100. The results show that applying the Standard and activating the emergency storage are effective in meeting the future water demand during extreme drought. In conclusion, we need to secure 110 million cubic meters (MCM) (Hapcheon reservoir) and 8 MCM (Gunwi reservoir) of water to reduce the number of days in the emergency stage. This research serves as a fundamental study that can help establish Standard and emergency storage activation criteria for other multipurpose reservoirs in preparation for extreme drought.

Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron

Machine learning (ML) has been a technique employed to build data-driven models that can map the relationship between the input and output data provided. ML-based data-driven models offer an alternative path to solving optimization problems, which are conventionally resolved by applying simulation models. Higher computational cost is induced if the simulation model is computationally intensive. Such a situation aptly applies to petroleum engineering, especially when different geological realizations of numerical reservoir simulation (NRS) models are considered for production optimization. Therefore, data-driven models are suggested as a substitute for NRS. In this work, we demonstrated how multilayer perceptron could be implemented to build data-driven models based on 10 realizations of the Egg Model. These models were then coupled with two nature-inspired algorithms, viz. particle swarm optimization and grey wolf optimizer to solve waterflooding optimization. These data-driven models were adaptively re-trained by applying a training database that was updated via the addition of extra samples retrieved from optimization with the proxy models. The details of the methodology will be divulged in the paper. According to the results obtained, we could deduce that the methodology generated reliable data-driven models to solve the optimization problem, as justified by the excellent performance of the ML-based proxy model (with a coefficient of determination, R2 exceeding 0.98 in training, testing, and blind validation) and accurate optimization result (less than 1% error between the Expected Net Present Values optimized using NRS and proxy models). This study aids in an enhanced understanding of implementing adaptive training in tandem with optimization in ML-based proxy modeling.

Investigation of the influences of asphaltene deposition on oilfield development using reservoir simulation

This paper investigates the deposition of asphaltenes in the porous medium of the studied field in Russia and predicts production profiles based on uncertainty evaluation. This problem can be solved by dynamic modeling, during which production profiles are estimated in two scenarios: with and without the activation of the asphaltene option. Calculations are carried out for two development scenarios: field operation under natural depletion and water injection into the aquifer as a reservoir pressure maintenance system. A full-scale compositional reservoir simulation model of the Russian oilfield was created. Within a dynamic simulation, the asphaltene option was activated and the asphaltene behavior in oil and porous medium was tuned according to our own special laboratory experiments. The model was also matched to production historical data, and a pattern model was prepared using the full-scale simulation model. Technological and the asphaltene option parameters were used in sensitivity and an uncertainty evaluation. Furthermore, probable production profiles within a forecast period were estimated. The sensitivity analysis of the pattern model to input parameters of the asphaltene option allowed determining the following heavy-hitters on the objective function: the molar weight of dissolved asphaltenes as a function of pressure, the asphaltene dissociation rate, the asphaltene adsorption coefficient and the critical velocity of oil movement in the reservoir. Under the natural depletion scenario, our simulations show a significant decrease in reservoir pressure and the formation of drawdown cones leading to asphaltene deposition in the bottom-hole area of production wells, decreasing their productivity. Water injection generally allows us to significantly reduce the volume of asphaltene phase transitions and has a positive effect on cumulative oil production. Injecting water into aquifer can keep the formation pressure long above the pressure for asphaltene precipitation, preventing the asphaltene deposition resulted from interaction of oil and water, so this way has higher oil production.

Advancing reservoir operations modelling in SWAT to reduce socio-ecological tradeoffs

The Soil & Water Assessment Tool (SWAT) is a useful model for evaluating socio-ecological tradeoffs and analyzing coupled natural-human system dynamics in agricultural watersheds. However, reservoir operating options in SWAT are limited. This study advances the representation of reservoir operations in SWAT by adding an option for closed-loop, multi-reservoir operating policies that can be optimized using Evolutionary Multi-Objective Direct Policy Search. This enables water managers to evaluate the tradeoffs across more coordinated reservoir operations in SWAT while capitalizing on the model's ability to physically simulate hydrological processes better than traditional reservoir simulation models. Comparing our advanced reservoir operations with SWAT's existing operating options in the Omo River basin of Ethiopia, we find a wider range of policies for managing conflicting stakeholder objectives that better compromise across them and are more robust to climate change. These advances to SWAT's reservoir module show promise for informing integrated water resources management.

An Improved Capillary Pressure Model for Fractal Porous Media: Application to Low-Permeability Sandstone

Capillary pressure is a crucial input in reservoir simulation models. Generally, capillary pressure measurements are expensive and time-consuming; therefore, there is a limitation on the number of cores tested in the laboratory. Accordingly, numerous capillary pressure models have been suggested to match capillary pressure curves and overcome this limitation. This study developed a new fractal capillary pressure model by depicting the porous system as a bundle of tortuous triangular tubes. The model imitates the pores’ angularity, providing a more accurate representation of the pore system than smooth circular openings. Moreover, triangular tubes allow the wetting phase to be retained in the tube’s corners. A genetic algorithm was employed to match the capillary pressure curves and obtain the proposed model’s parameters. Capillary pressure data of eight low-permeability sandstone samples from the Khatatba formation in the Western Desert of Egypt were utilized to test the proposed model. The results revealed that the developed model reasonably matched the laboratory-measured data.

Unsteady-State CO2 Foam Generation and Propagation: Laboratory and Field Insights

This work presents a multiscale experimental and numerical investigation of CO2 foam generation, strength, and propagation during alternating injection of surfactant solution and CO2 at reservoir conditions. Evaluations were conducted at the core-scale and with a field-scale radial simulation model representing a CO2 foam field pilot injection well. The objective of the experimental work was to evaluate foam generation, strength, and propagation during unsteady-state surfactant-alternating-gas (SAG) injection. The SAG injection rapidly generated foam based upon the increased apparent viscosity compared to an identical water-alternating-gas (WAG) injection, without surfactant. The apparent foam viscosity of the SAG continually increased with each subsequent cycle, indicating continued foam generation and propagation into the core. The maximum apparent viscosity of the SAG was 146 cP, whereas the maximum apparent viscosity of the WAG was 2.4 cP. The laboratory methodology captured transient CO2 foam flow which sheds light on field-scale CO2 foam flow. The single-injection well radial reservoir simulation model investigated foam generation, strength, and propagation during a recently completed field pilot. The objective was to tune the model to match the observed bottom hole pressure data from the foam pilot and evaluate foam propagation distance. A reasonable match was achieved by reducing the reference mobility reduction factor parameter of the foam model. This suggested that the foam generated during the pilot was not as strong as observed in the laboratory, but it has propagated approximately 400 ft from the injection well, more than halfway to the nearest producer, at the end of pilot injection.

Quantity–quality-based water allocation programming in a reservoir–river system

Abstract Simulation–optimization approaches are useful methods for the assessment of water resource engineering plans and finding the best management policy at the watershed scale. In this study, to find the optimum operation for a reservoir with the purpose of satisfying water demands while meeting the water quantity and quality criteria, a generic reservoir and river basin simulation model (MODSIM) is coupled with the particle swarm optimization (PSO) algorithm leading to construct the PSO–MODSIM model. With the decision variables of the reservoir's monthly releases, the objective function is to maximize the supply for downstream demands while keeping the electrical conductivity (EC) in the river flow lower than a predefined level at the downstream checkpoint, which is a function of the EC in the agricultural return flows. Moreover, a safe flow rate is defined in which the streamflow should not exceed at the checkpoint resulting in mitigation of the submerging lands damage. Results obtained by the PSO–MODSIM model indicate the ability of the proposed simulation–optimization approach for solving the problem of optimal quantity–quality-based water allocation in a reservoir–river system. For instance, the EC at the checkpoint is decreased by 61% in the optimum reservoir operation state comparing the present situation, whereas the municipal and environmental demands are fully met and the agricultural demands are supplied with a desirable reliability satisfaction level.

CO2 Sequestration and Enhanced Oil Recovery via the Water Alternating Gas Scheme in a Mixed Transgressive Sandstone-Carbonate Reservoir: Case Study of a Large Middle East Oilfield

The knowledge on CO2 sequestration and CO2 enhanced oil recovery (CO2-EOR) in mature mixed and interbedded hydrocarbon reservoirs are limited. In this vein, the feasibility of CO2-water alternating gas (CO2-WAG) for coupling CO2 sequestration and CO2-EOR in a mature mixed sandstone-carbonate reservoir was investigated using the S1A reservoir. First, core sample analysis and scanning electron microscopy (SEM) were conducted to evaluate the reservoir characterization. Next, a geological model with dual porosity and permeability was developed and transferred to a reservoir simulation model, and 21-year field production data were utilized for history matching and constraining of the reservoir model. Then, continuous CO2 and CO2-WAG injection methods were simulated using newly developed and history-matched geo-models and compared to assess their CO2-EOR and CO2 storage mechanisms and determine their potential in a mixed sandstone-carbonate reservoir. The effect of the anisotropic permeability ratio and hysteresis on CO2 storage mechanisms was addressed in this work. The results indicate that the CO2-WAG scheme can yield a +3% oil recovery factor than the continuous CO2 injection method, and CO2-WAG injection can utilize up to 14 and 12% of the total CO2 injected for residual and solubility trappings, respectively, while a minimal 2.9 and 0.03% was utilized from continuous CO2 injection for residual and solubility trappings, respectively. The WAG ratio of 2:1 could yield a higher recovery factor and greater CO2 utilization for solubility and residual trappings in a mixed reservoir. In mixed and interbedded reservoirs, geological anisotropy can also strongly influence reservoir performance during the CO2-EOR process, in which higher values of the anisotropy ratio (Kv/Kh) in CO2-WAG can yield greater oil recovery and more CO2 storage; also, hysteresis has great impact on residual trapping. This study provides valuable insights into the potential of CO2-WAG for CO2 sequestration and CO2-EOR in mature mixed and interbedded reservoirs.

New Insights into the Understanding of In-Situ Combustion: Important Considerations When Modeling the Process

Summary Air-injection-based enhanced oil recovery (EOR) processes have historically been of great interest due to their high recovery potential and applicability to a wide range of reservoirs where other processes are not effective or economical. However, most operators require a certain level of confidence in the potential recovery from these (or any) process before committing resources; this is commonly achieved with the support of laboratory and reservoir simulation studies. Laboratory testing, including combustion tube, ramped temperature oxidation (RTO), and accelerating rate calorimeter (ARC) tests, can supply data for simple analytical models. It can also provide important insights into potential oxidation behaviors and oil recovery mechanisms. Similarly, reservoir simulation of some of those experiments can assist in the understanding of the process and may allow for the development of kinetic models that can be used for further reservoir modeling. However, due to sample size limitation and the unscaled nature of the experiments, these tests are not ideally suited to provide detailed or unique kinetic data for direct use in numerical simulators. In fact, the oxidation reactions are sufficiently complex that, regardless of how robust a thermal reservoir simulator may be, its predictive capability strongly depends on the engineer’s understanding of the process and ability to model the most relevant oxidation behaviors of the particular hydrocarbon reservoir under study. Over the past 50 years, the In-Situ Combustion Research Group (ISCRG) at the University of Calgary has dedicated its efforts toward the advancement of this technology. Under the leadership of Professor Gordon Moore, the ISCRG has performed a large number of combustion tests, designed and carried out many novel oxidation experiments, and also made important contributions to the numerical modeling of air-injection-based processes. Nevertheless, in spite of its long research history, the group acknowledges that there is still much that needs to be learned about the process. For example, two oils with the same physical properties such as viscosity and density can have significantly different oxidation behaviors, which are difficult to predict; this is one of the reasons the group continues to perform laboratory experiments and conduct research in this area. This paper describes some of the most important conceptual contributions made by the ISCRG based on their experimental results and how they have enhanced our understanding of the process. These continue to be an important source of knowledge toward the development of predictive reservoir simulation models, as it is very difficult, if not impossible, to properly model a physical problem one does not understand well. For instance, the fundamental equations used for mathematical modeling depend on selecting of the relevant physical mechanisms and assumptions made, and these are derived from experimental work. Similarly, when using a commercial numerical simulator, the selection of fluid pseudocomponents as well as their physical properties and chemical reactions, as well as their kinetic parameters, also depend on an understanding of the process. This paper provides a summary of the relevant physical aspects to consider when modeling the in-situ combustion (ISC) process as well as new insights on its dynamics based on the laboratory experiments performed by the ISCRG.

GAS-CONDENSATE FLUID PVT MODEL QUALITY CHECK BASED ON THE CONCEPT OF A SINGLE-CELL SIMULATION MODEL

The problems of gas-condensate PVT-models (Pressure Volume Temperature, PVT) creation under limited input information were analyzed. Traditional fluid phase behavior characterization approach relies on creation of the equation of state (EOS) based on initial composition of reservoir fluid and its future regression for critical parameters (pressure and temperature), binary interaction coefficients, acentric factors of residual “plus” fraction or pseudo-components. The adjustment is done until the moment when EOS is reproducing the results of laboratory experiments. Classic PVT experiments performed on gas-condensates and volatile oils are constant composition expansion (CCE), constant volume depletion (CVD) and separator tests. However, in the case of most Ukrainian fields, discovered and explored in the last century, not only the reliable detailed initial fluid composition is not available, but phase behavior was studied with non-equilibrium method of so-called differential condensation, that does not allow their direct application for PVT models creation. Previously, the authors [1, 2] presented an alternative method for fluid characterization based on the fractional distillation test. At the same time, due to significant uncertainty in input data, particularly a) condensate production allocation; b) commingled production from multiple reservoirs with different C5+ yield; c) non-recorded change of separator conditions that affects liquid extraction and its density; d) technological production losses, issues of reproducing the condensate production during history matching of several models of Dniper-Donetsk Basin were faced. There was proposed and explained in detail an example of single-cell reservoir simulation model application concept for quality check of created PVT model for one of the fields with potential yield of 86 g/m3. The idea of the concept is based on the reproduction of material balance of gas-condensate reservoir through one conditional well controlled on a primary (gas) phase, that allows quick identification of changes into calculated gas-condensate yield curve, necessary for matching of condensate production. Implementation of these changes allows quick and precise full-field model calibration.