Numerical Reservoir Simulation Research Articles

Energy-consumption calculation and optimization method of integrated system of injection-reservoir-production in high water-cut reservoir

In recent years, the world's crude-oil production has stopped increasing. Many oilfields have entered the high water-cut stage. Therefore, it is very important to reduce the energy consumption of high water-cut oilfield under limited oil production. In view of the lack of research on the overall energy consumption of the entire oilfield, three subsystems of injection, reservoir, and production are coupled through the node analysis method. The integrated energy-consumption calculation model of the injection–reservoir–production (I–R–P) coupling system is established. Finally, in order to optimize the energy consumption of the oilfield, the production scheme is optimized by combining Particle Swarm Optimization algorithm, I–R–P integration system, and reservoir numerical simulation. The optimal injection–production ratio is 1.088 in the pressure adjustment stage, the optimal target formation pressure is 15.8 MPa, and the optimal energy consumption per ton of oil is 280.15 kW h. Compared with the initial production scheme, the optimal energy consumption of I–R–P integrated system is reduced by more than 8%, which can effectively achieve the purpose of energy savings and consumption reduction. The optimization scheme has been applied in the actual field, the annual power savings could reach 6.23 × 106 kW h.

A simple approach to increasing computational efficiency of numerical simulations of hydrocarbon migration based on the Darcy flow concept

One of the most sophisticated and significant stages of basin modelling is simulation of hydrocarbon (HC) migration. At this stage, the attempt is made to estimate the probability of HCs existence in the region of interest. This can be done by utilizing readily available numerical reservoir simulation tools. Such tools are typically based on the Darcy flow model and are more frequently used for less time-consuming procedures. For instance, identification of the structural features of the formation and estimation of the production profiles. Despite all the advantages of this approach, there are some significant drawbacks including the extended computational time required to simulate the hydrodynamic process of HC migration. This issue cannot always be resolved by increasing computational power due to technological limitations. Thus, the authors suggest a method of the numerical scheme by splitting it along the coordinates (further called the NSS-method), allowing for a significant reduction in the computational time when modelling HC migration. Explicit and implicit numerical models are implemented and scrutinized for the purpose of this research. The validity of these models is verified by their comparison with the available analytical solutions and by analysing the stability of these numerical schemes. As a result, the influence of the NSS-method on the accuracy of the calculation was insignificant, allowing a decrease in the computational time by several hundred times.

Study on characterization model of relative permeability based on three parameter Weibull model

Aiming at the problem of large error between relative permeability and water cut in high water cut and low water cut stage. A large number of experiments show that there is a good linear relationship between the double logarithm of water saturation and the logarithm of oil-water relative permeability ratio. On this basis, the three parameter Weibull model is applied to establish a new characterization model of relative permeability. The calculation results of two examples show that the calculated values of the new model are all close to the actual values in low water cut, medium water cut and high water cut stages, and the accuracy is high. This study provides a basis for improving the accuracy of reservoir engineering scheme design and reservoir numerical simulation.

The Adverse Impact of Muddy Laminae on Conventional SAGD Performance and Improvement Strategy with the Multilateral Well Pattern

With further progress of Steam-Assisted Gravity Drainage (SAGD) technology, a growing number of oil sands or heavy oil reservoirs were put into production in an efficient way. However, owing to the existence of muddy laminae within reservoirs, there are challenges associated with the expansion of the steam chamber and oil drainage during the SAGD process. The purpose of this study is to evaluate the adverse impact of muddy laminae on conventional SAGD performance and introduce an improvement strategy with multilateral well patterns to reduce the adverse impact and improve the performance. In the research reported here, the reservoir numerical simulation approach is applied to conduct the research. The analysis conducted on a prototypical reservoir reveals that the steam chamber may expand slowly in some sections due to the poor capacity of heat and mass transfer, and the expansion of the steam chamber is relatively uneven along the wellbore, when the muddy laminae are existing in the formation. The influence level of the muddy laminae on conventional SAGD performance under different distribution modes is different, but the adverse effect is mainly reflected in the delay of peak oil production, the decrease in peak oil production, the decrease in steam chamber volume, and the increase in the cumulative steam oil ratio (mainly in early and middle stages of the SAGD process). On the basis of aforementioned researches, the improvement strategy with two different multilateral well patterns, planar multilateral well and upward multilateral well, is introduced to improve the SAGD performance. The results indicate that the combination of a planar multilateral injector and planar multilateral producer has the best performance. By adopting such kind of combination, the recovery factor can be increased from 31.36% to 47.08%, and the cumulative steam oil ratio can be decreased from 5.29 m3/m3 to 4.64 m3/m3 under the combined distribution mode of muddy laminae. It can be known that the branches of the planar multilateral well are very helpful for the expansion of the steam chamber and oil drainage, once the heat connection between branches of the injector and producer is well established. Overall results show that the multilateral well pattern is promising for SAGD applications at oil sands or heavy oil reservoirs which are rich in muddy laminae.

Comprehensive Characterization Investigation of Multiple Time-Varying Rock-Fluid Properties in Waterflooding Development

Abstract In this paper, the characterization parameter “effective displacement flux” is employed to describe the flushing intensity, and a new numerical simulator in which the rock-fluid properties considered functions of the effective displacement flux is developed based on the black oil model. Additionally, a conceptual reservoir model is established to validate the effective characterization of the time-varying mechanisms: the time-varying oil viscosity can characterize the viscous fingering of the water phase and the time-varying absolute permeability can present the aggravation of reservoir heterogeneity, the alteration of wettability is characterized with the time-varying relative permeability, and the ultimate recovery will increase with the combined effect of all three time-varying factors. Eventually, the new simulator is applied to the simulation of an actual waterflooding reservoir to illustrate the assistance in history matching. The simulation results of our simulator can readily match the history data, which proves that the consideration of comprehensive time-varying rock-fluid properties can significantly improve the accuracy during the numerical simulation of waterflooding reservoirs.

A Novel Shale Gas Production Prediction Model Based on Machine Learning and Its Application in Optimization of Multistage Fractured Horizontal Wells

Shale gas production prediction and horizontal well parameter optimization are significant for shale gas development. However, conventional reservoir numerical simulation requires extensive resources in terms of labor, time, and computations, and so the optimization problem still remains a challenge. Therefore, we propose, for the first time, a new gas production prediction methodology based on Gaussian Process Regression (GPR) and Convolution Neural Network (CNN) to complement the numerical simulation model and achieve rapid optimization. Specifically, through sensitivity analysis, porosity, permeability, fracture half-length, and horizontal well length were selected as influencing factors. Second, the n-factorial experimental design was applied to design the initial experiment and the dataset was constructed by combining the simulation results with the case parameters. Subsequently, the gas production model was built by GPR, CNN, and SVM based on the dataset. Finally, the optimal model was combined with the optimization algorithm to maximize the Net Present Value (NPV) and obtain the optimal fracture half-length and horizontal well length. Experimental results demonstrated the GPR model had prominent modeling capabilities compared with CNN and Support Vector Machine (SVM) and achieved the satisfactory prediction performance. The fracture half-length and well length optimized by the GPR model and reservoir numerical simulation model converged to almost the same values. Compared with the field reference case, the optimized NPV increased by US$ 7.43 million. Additionally, the time required to optimize the GPR model was 1/720 of that of numerical simulation. This work enriches the knowledge of shale gas development technology and lays the foundation for realizing the scale-benefit development for shale gas, so as to realize the integration of geological engineering.

Analytical solution for one-dimensional three-phase incompressible flow in porous media for concave relative permeability curves

Three-phase flow in porous media may appear in different scenarios during the production life of a hydrocarbon reservoir. The simultaneous flow of different phases is modeled by relative permeability curves which are fundamental to petroleum production analysis and forecast. Laboratory experiments are the main source of data for relative permeability curves. Mathematical solutions for multiphase flow in porous medium are key for determining relative permeability curves from laboratory data, to check numerical reservoir simulation results and for screening an enhanced oil recovery technique. The complexity of reservoir modeling and the use of numerical optimization to history match the laboratory data have shown the importance of concave relative permeability curves. In this paper we present the analytical solution for one-dimensional incompressible immiscible three-phase flow in porous media, where the relative permeability functions are described by concave curves. The hyperbolic system of equations that results from mass conservation is solved by the method of characteristics. The results show close agreement to numerical solutions.

Prediction of shale oil production based on Prophet algorithm

The large-scale volume fracturing development of shale oil horizontal wells, and the production is affected by seasonal cycle and emergencies, resulting in the complexity of production prediction. Aiming at the problem of poor actual effect or heavy workload of shale oil production prediction by classical reservoir engineering, reservoir numerical simulation and other methods, a new shale oil production prediction method based on Prophet algorithm is proposed It is easier to find the inherent law from a small amount of data. In this paper, we use the production data of production wells in Huanjiang A reservoir in 2015 to predict the production, and compare the prediction results with long-term and short-term memory neural network (LSTM) and ARPS production decline model. The results show that the prediction accuracy of Prophet algorithm is higher, and it is more accurate for the production of complex shale oil.

Stochastic Techno-economic Analysis of CO2-circulated Geothermal Energy Production in a Closed Reservoir System

Many wells become abandoned with the depletion of oil and gas reservoirs, and low-enthalpy geothermal energy could be extracted from these abandoned wells. CO2 has been proposed and studied as the working fluid for mining such geothermal energy sources, owing to its favorable thermodynamic properties. The objective of this study is to develop an uncertainty-based simulation-optimization approach to analyze the techno-economic feasibility of CO2-circulated geothermal production in a closed reservoir system. In doing so, we address the particular geothermal reservoir conditions found in North Oman`s foreland basin where several large oil/gas fields are located. The geothermal reservoir conditions in this area can be characterized by inclined and thin layers, partially to fully fault-blocked, and relatively homogeneous and low permeable formations. Such conditions have not been investigated in the context of CO2-circulated geothermal production. Another key novelty of this work is to analyze a triplet horizontal well placement designed particularly for such thinly-bedded fully-closed reservoirs. The main building blocks of the proposed methodology are the application of a non-isothermal, multi-phase, multi-component numerical reservoir simulator, a neural network-based metamodel, Monte Carlo simulations, and genetic algorithm. The findings suggest the proposed well pattern is technically feasible and economically viable to harvest low-enthalpy geothermal energy from the type of depleted reservoirs found in North Oman region.

Numerical Modelling on CO2 Storage Capacity in Depleted Gas Reservoirs

Making an accurate estimate of the CO2 storage capacity before the commencement of a carbon capture and storage (CCS) project is crucial to the project design and feasibility investigation. We present herein a numerical modelling study on the CO2 storage capacity in depleted gas reservoirs. First, we show a simple volumetric equation that gives the CO2 storage capacity in a depleted gas reservoir, which considers the same volume of CH4 at reservoir pressure and temperature conditions produced from the reservoir. Next, the validity and the limitations of this equation are investigated using a numerical reservoir simulation with the various reservoir characteristics of reservoir heterogeneity, aquifer water encroachment, and rock compaction and its reversibility. Regardless of the reservoir heterogeneity, if a reservoir is subjected to a weak or moderate aquifer support, the volumetric equation provides an estimate of the CO2 storage capacity as structurally trapped gas within 1% of that estimated from numerical simulations. The most significant factor influencing the CO2 storage capacity is the reversibility of rock compaction, rather than the degree of rock compaction. If reservoir rocks have a strong hysteresis in their compaction and expansion behaviour, the material balance equation will overestimate the amount of structural CO2 trapping. All the simulation results show a fairly consistent amount of trapped CO2 as a dissolved component in water, which is 15∼17% of the structurally trapped CO2. Overall, our study presents the validity and the limitation of the simple material balance equation for estimating the CO2 storage capacity, which helps with designing a CCS project at the early stage.

Technology Focus: Reservoir Simulation (July 2021)

In reviewing the long list of papers this year, it has become apparent to me that the hot topic in reservoir simulation these days is the application of data analytics or machine learning to numerical simulation and with it quite often the promise of data-driven work flows—code for needing to think about the physics less. Data-driven work flows have their place, especially when we have a lot of data and the system is very complex. I’m thinking shales especially, but seeing it being applied to more conventional reservoirs gave me a moment of pause. I can’t help but think that, in terms of the hype cycle as related to the application of machine learning to numerical simulation, we may be approaching the peak of inflated expectations. I say “approaching,” because many companies appear to be dipping their toes in the water, perhaps because they think they should, but few are truly committing to it. Many vocal champions of the approach exist, but most decision-makers just don’t understand it yet. If we cannot explain how something works simply, then thoughtful leaders will tend not to trust it. Whether it be numerical-simulation findings or self-organizing neural networks, the need will always exist for a deep understanding and clarity of explanation of both the discipline and method used. To decision-makers, it will be an attractive concept, but they will generally ask to validate against more traditional methods. I look forward to a future when we are through the trough of disillusionment and start climbing the slope of enlightenment to a new level of productivity. I suspect, though, that it will take at least another 5 years, as our current crop of knowledgeable evangelists become decision-makers themselves and can put in place work flows and teams to leverage the approach appropriately for their problems, intelligently leveraging their years of hard-won experience. I will lay a wager with you, though, that when that time comes, those new ways of working more efficiently will rely just as much if not more upon a deep understanding of reservoir engineering as our current methods. I hope you enjoy these papers, which include examples of both the new approach as well as tried-and-true approaches. Recommended additional reading at OnePetro: www.onepetro.org. SPE 202436 - Fast Modeling of Gas Reservoirs Using Proper Orthogonal Decomposition/Radial Basis Function (POD/RBF) Nonintrusive Reduced-Order Modeling by Jemimah-Sandra Samuel, Imperial College London, et al. IPTC 21417 - A New Methodology for Calculating Wellbore Shut-In Pressure in Numerical Reservoir Simulations by Babatope Kayode, Saudi Aramco, et al. SPE 201658 - Mechanistic Model Validation of Decline Curve Analysis for Unconventional Reservoirs by Mikhail Gorditsa, Texas A&M University, et al.

Sustainability of geothermal development strategy using a numerical reservoir modeling: A case study of Tompaso geothermal field

Numerical reservoir simulation is a fundamental instrument to assess geothermal energy development strategy. This study aimed to optimize geothermal project development by integrating numerical modeling, reserve estimation, and geothermal power plant thermodynamics. Therefore, a case study for a numerical reservoir model was built based on the published data of the Tompaso field in North Sulawesi, Indonesia. The Tompaso field model was built in the TOUGH2 simulator, and the output was validated using observed data of 10 wells. The deviation of temperature and pressure models against the observed data ranged from 4.1 to 20.2 °C and 1.2–18 bar, respectively, so that the model is believed to reach the natural state condition. The natural state model was the basis for calculating the proven reserve using the dynamic deliverability method and approach of experimental design Box-Behnken 2nd order. A proxy equation generated by 27 simulations was combined with Monte Carlo simulations yielding a proven reserve of 62.2 MWe (P50) for the Tompaso field. Two development scenarios, full-scale and stepwise, were selected and simulated to determine the requirement of production, reinjection, and make-up wells for each strategy. It was found that using a stepwise development strategy can reduce the required amount of reinjection and make-up wells.

Reservoir Simulation, Modeling of a Complex Offshore Field Uses AI and Machine Learning

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201693, “Subsurface Analytics Case Study: Reservoir Simulation and Modeling of a Highly Complex Offshore Field in Malaysia Using Artificial Intelligence and Machine Learning,” by Rahim Masoudi, SPE, Petronas; Shahab D. Mohaghegh, SPE, West Virginia University; and Daniel Yingling, Intelligent Solutions, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, 5–7 October. The paper has not been peer reviewed. Using commercial numerical reservoir simulators to build a full-field reservoir model and simultaneously history matching multiple dynamic variables for a highly complex offshore mature field in Malaysia had proved challenging. In the complete paper, the authors demonstrate how artificial intelligence (AI) and machine learning can be used to build a purely data-driven reservoir simulation model that successfully history matches all dynamic variables for wells in this field and subsequently can be used for production forecasting. This synopsis concentrates on the process used, while the complete paper provides results of the fully automated history matching. Subsurface Analytics In the presented technique, which the authors call subsurface analytics, data-driven pattern-recognition technologies are used to embed the physics of the fluid flow through porous media and to create a model through discovering the best, most-appropriate relationships between all measured data in each reservoir. This is an alternative to starting with the construction of mathematical equations to model the physics of the fluid flow through porous media, followed by modification of geological models in order to achieve history match. The key characteristics of subsurface analytics are that no interpretations, assumptions, or complex initial geological models (and thus no upscaling) exist. Furthermore, the main series of dynamic variables used to build this model is measured on the surface, while other major static, and sometimes even dynamic, characteristics are based on subsurface measurements, thereby making this approach a combination of reservoir and wellbore-simulation models rather than merely a reservoir model. The history-matching process of the subsurface analytics process is completely automated. Top-Down Modeling (TDM) TDM is a data-driven reservoir modeling approach under the realm of subsurface analytics technology that uses AI and machine learning to develop full-field reservoir models based on measurements rather than solutions of governing equations. TDM integrates all available field measurements into a full-field reservoir model and matches the historical production of all individual wells in a mature field with a single AI-based model. The model is validated through blind history matching. The approach then can forecast a field’s behavior on a well-by-well basis. TDM is a data-driven approach; thus, the quality assurance/quality control (QA/QC) of the data input is para-mount before embarking on the modeling process to ensure that the artificial neural network (ANN) is taught properly with reliable training of the data set. This includes the understanding of data availability and magnitude, analysis of well-by-well production performance trends, and identification of data anomalies.

A new-generation Embedded Discrete Fracture Model calibration workflow applied to the characterization of complex naturally fracture reservoir

Characterizing natural fractures has a decisive effect on production forecasts in fractured oil and gas reservoirs. Discrete Fracture Networks (DFN) constitutes the main modeling framework for fractured geosystems. However, myriads of uncertainties are enclosed prior modeling representative stochastic or deterministic DFN ensembles. This paper presents a novel methodology for DFN calibration and an efficacious field application, which incorporates Well-testing interpretation, Embedded Discrete Fracture Model (EDFM) framework, and numerical reservoir simulation. The proposed workflow starts with the DFN generation from seismic data, imaging logging data and core data. After multiple DFNs are modeled, well-test analysis is employed to calibrate the intrinsic properties of fractures at different locations. Then, these fracture networks are characterized dynamically by EDFM, which promotes capturing the optimal fracture model quickly screened. Finally, pressure and production history match are reached for the DFN realization that honors the optimal fracture model.

Risk‐based area of review estimation in overpressured reservoirs to support injection well storage facility permit requirements for CO2storage projects

AbstractThis paper presents a workflow for delineating a risk‐based area of review (AOR) to support a US Environmental Protection Agency (EPA) Class VI permit for a carbon dioxide (CO2) storage project. The approach combines semianalytical solutions for estimating formation fluid leakage through a hypothetical leaky wellbore with the results of physics‐based numerical reservoir simulations. The workflow is demonstrated using a case study for a hypothetical 180,000‐metric‐ton‐per‐year storage project located in the Plains CO2Reduction (PCOR) Partnership region, which includes all or part of 10 states in the United States and four Canadian provinces. Under the scenario where the leaky wellbore is open to a saline aquifer (thief zone) between the overlying seal (cap rock) and the underground sources of drinking water (USDW), the risk‐based AOR is no larger than the areal extent of the CO2plume in the storage reservoir because the pressure buildup in the storage reservoir beyond the CO2plume is insufficient to drive formation fluids up a hypothetical leaky wellbore into the USDW. However, even under the conservative assumption that the leaky wellbore is not open to a thief zone, the incremental leakage beyond the areal extent of the CO2plume is less than 400 m3over 20 years. The approach outlined in this paper is designed to be protective of USDWs and comply with the Safe Drinking Water Act requirements and provisions for the EPA Class VI Underground Injection Control (UIC) Program (Class VI Rule) and North Dakota Administrative Code Chapter 43‐05‐01. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd.

Prediction of Comprehensive Water Cut in Periodic Waterflood Reservoir Based on Variable Weight Combination Model

The comprehensive water cut of periodic waterflood reservoirs is random and volatile, and it is a non-stationary time series. In order to solve the problems of poor prediction results of conventional methods or heavy workload and long time-consuming in reservoir numerical simulation, an EMD-LSTM neural network model and a smoothing spline regression (Smoothing Spline) variable weight combined prediction model are proposed. This method introduces empirical mode decomposition (EMD) to process non-stationary and non-linear data, and combines the advantages of machine learning and curve regression to improve the prediction accuracy of the model. The variable weight combination model is used to predict the water content of the periodic water injection reservoir. The results show that the prediction accuracy of the variable weight combination model is significantly higher than that of the single model. The variable weight combination model can reduce the prediction error and can effectively and accurately predict the periodic water injection oil. The comprehensive water content of the reservoir can guide the adjustment of water injection parameters for periodic water injection reservoirs.

Characterization of complex hydraulic fractures in Eagle Ford shale oil development through embedded discrete fracture modeling

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

Physics-informed deep learning for prediction of CO2 storage site response

Accurate prediction of the CO2 plume migration and pressure is imperative for safe operation and economic management of carbon storage projects. Numerical reservoir simulations of CO2 flow could be used for this purpose allowing the operators and stakeholders to calculate the site response considering different operational scenarios and uncertainties in geological characterization. However, the computational toll of these high-fidelity simulations has motivated the recent development of data-driven models. Such models are less costly, but may overfit the data and produce predictions inconsistent with the underlying physical laws. Here, we propose a physics-informed deep learning method that uses deep neural networks but also incorporates flow equations to predict a carbon storage site response to CO2 injection. A 3D synthetic dataset is used to show the effectiveness of this modeling approach. The model approximates the temporal and spatial evolution of pressure and CO2 saturation and predicts water production rate over time (outputs), given the initial porosity, permeability and injection rate (inputs). First, we establish a baseline using data-driven deep learning models namely, Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM). To build a physics-informed model, the loss term is modified using the constraints defined by a simplified form of the governing partial differential equations (conservation of mass coupled with Darcy's law for a two-phase flow system). Our results indicate that incorporating the domain knowledge significantly improves the accuracy of predictions. The proposed modeling approach can be integrated in CO2 storage management to accurately predict the critical site response indicators for a range of relevant input parameters, even when limited training data is available.

A qualitative study of the impact of random shale barriers on SAGD performance using data analytics and machine learning

Shale barriers are common in all types of oil and gas reservoirs. Previous works on characterizing the impact of random shales are limited. Although for a gravity-based recovery method such as steam-assisted gravity drainage (SAGD), these shale barriers prove to be a major hurdle in production rate and ultimate oil recovery. Apart from obstructing the oil drainage path, shale barriers may also contribute to the additional heat losses. Hence characterizing these shale barriers is of importance to understand the magnitude of their impact on oil recovery. In this paper, first, we describe a procedure to construct the base 2D reservoir model and then populate it log-normally with various scenarios of random shales based on the mean and standard deviation. Unique features are extracted to parameterize these random shales. Next, the impact of reservoir heterogeneity on the recovery factor is described qualitatively using these shale features. Second, we prepare a non-linear model using a machine learning algorithm for predicting oil recovery factor using a synthetic dataset. We demonstrate that the machine learning approach can predict the oil recovery factor with reasonable accuracy compared with thermal numerical reservoir simulation results. The model resulted in an R2 value of 0.95 and RMSE of 5.06. Considering the complexity of characterization of randomly distributed shales, this is a considerable accuracy from a model trained on 226 simulation data sets. The approach described in this paper can be used as a technique to characterize the impact of randomly distributed shales. The predictive model may also be utilized to run a large set of sensitivity, which can prove effective for making a real-time decision. These techniques can be further advanced with a larger set of training data, including a variety of dynamic and static reservoir properties.

Study on Viscosity Reducer Flooding Technology for Deep Low Permeability Extra Heavy Oil Reservoirs

Deep low permeability extra heavy oil reservoir has the characteristics of high formation pressure, high crude oil viscosity, and low permeability. Conventional steam injection thermal recovery has poor viscosity reduction performance and low productivity of a single well, which makes it difficult to develop this type of heavy oil reservoir. In this paper, core flooding experiment and microvisualization equipment were used to study the mechanism of improving the recovery of deep extra heavy oil by using water-soluble viscosity reducer; the realization of water-soluble viscosity reducer in numerical simulation was achieved by using nonlinear mixing rule; the reservoir numerical simulation model of water-soluble viscosity reducer displacement in test well group was established to optimize the development technical parameter of water-soluble viscosity reducer. The results show that compared with waterflooding, the oil displacement efficiency of water-soluble viscosity reducer is increased by 12.7%; water-soluble viscosity reducer can effectively reduce the viscosity of extra heavy oil, under the same temperature and permeability, the higher the concentration of viscosity reducer, the better the viscosity reduction effect, and the smaller the pressure gradient required at the same injection rate; the main mechanism of water-soluble viscosity reducer for enhancing oil recovery is to form oil in water emulsion, which can reduce the viscosity and interfacial tension of crude oil and reduce the residual oil saturation; in the pilot well group, the optimized injection concentration of water-soluble viscosity reducer is 3%, and the optimal injection amount of water-soluble viscosity reducer solution is 50 t/d; water-soluble viscosity reducer displacement was implemented in the pilot well group, the average daily oil of well group was increased from 1.8 t/d to 7.34 t/d, and the pilot well group has achieved good development performance.