History Matching and Production Prediction of Steam Drive Reservoir Based on Data-Space Inversion Method

Recently, a data-space inversion (DSI) method has been proposed and successfully applied for the history matching and production optimization for conventional waterflooding reservoir. Under Bayesian framework, DSI can directly and effectively obtain posterior flow predictions without inverting any geological parameters of reservoir model. In this paper, we integrate the numerical simulation model with DSI method for rapid history matching and production prediction for steam flooding reservoir. Based on the finite volume method, a numerical simulation model is established and it is used to provide production data samples for DSI by the simulation of ensemble geological models. DSI-based production prediction model is then established and get trained by the historical data through the random maximum likelihood principle. The posterior production estimation can be obtained fast by training the DSI-based model with history data, but without any posterior geological parameters. The proposed method is applied for history matching and estimating production performance prediction in some numerical examples and a field case, and the results prove its effectiveness and reliability.

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History matching and production optimization of water flooding based on a data-driven interwell numerical simulation model

Journal of Natural Gas Science and Engineering ◽

10.1016/j.jngse.2016.02.043 ◽

2016 ◽

Vol 31 ◽

pp. 48-66 ◽

Cited By ~ 10

Author(s):

Hui Zhao ◽

Ying Li ◽

Shuyue Cui ◽

Genhua Shang ◽

Albert C. Reynolds ◽

...

Keyword(s):

Numerical Simulation ◽

Simulation Model ◽

History Matching ◽

Production Optimization ◽

Data Driven ◽

Water Flooding

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An improved data space inversion method to predict reservoir state fields via observed production data

Petroleum Science ◽

10.1016/j.petsci.2021.07.008 ◽

2021 ◽

Author(s):

Deng Liu ◽

Xiang Rao ◽

Hui Zhao ◽

Yun-Feng Xu ◽

Ru-Xiang Gong

Keyword(s):

Production Data ◽

Inversion Method ◽

Data Space ◽

Reservoir State ◽

Space Inversion

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An Inversion Method of Relative Permeability Curves in Polymer Flooding Considering Physical Properties of Polymer

SPE Journal ◽

10.2118/189980-pa ◽

2018 ◽

Vol 23 (05) ◽

pp. 1929-1943 ◽

Cited By ~ 4

Author(s):

Yongge Liu ◽

Jian Hou ◽

Lingling Liu ◽

Kang Zhou ◽

Yanhui Zhang ◽

...

Keyword(s):

Numerical Simulation ◽

Simulation Model ◽

Relative Permeability ◽

Polymer Flooding ◽

Inversion Method ◽

Residual Resistance ◽

Relative Permeability Curve ◽

Nonsteady State ◽

State Method ◽

Relative Permeability Curves

Summary Reliable relative permeability curves of polymer flooding are of great importance to the history matching, production prediction, and design of the injection and production plan. Currently, the relative permeability curves of polymer flooding are obtained mainly by the steady-state, nonsteady-state, and pore-network methods. However, the steady-state method is extremely time-consuming and sometimes produces huge errors, while the nonsteady-state method suffers from its excessive assumptions and is incapable of capturing the effects of diffusion and adsorption. As for the pore-network method, its scale is very small, which leads to great size differences with the real core sample or the field. In this paper, an inversion method of relative permeability curves in polymer flooding is proposed by combining the polymer-flooding numerical-simulation model and the Levenberg-Marquardt (LM) algorithm. Because the polymer-flooding numerical-simulation model by far offers the most-complete characterization of the flowing mechanisms of polymer, the proposed method is able to capture the effects of polymer viscosity, residual resistance, diffusion, and adsorption on the relative permeability. The inversion method was then validated and applied to calculate the relative permeability curve from the experimental data of polymer flooding. Finally, the effects of the influencing factors on the inversion error were analyzed, through which the inversion-error-prediction model of the relative permeability curve was built by means of multivariable nonlinear regression. The results show that the water relative permeability in polymer flooding is still far less than that in waterflooding, although the residual resistance of the polymer has been considered in the numerical-simulation model. Moreover, the accuracy of the polymer parameters has great effect on that of the inversed relative permeability curve, and errors do occur in the inversed water relative permeability curve—the measurements of the polymer solution viscosity, residual resistance factor, inaccessible pore-volume (PV) fraction, or maximum adsorption concentration have errors.

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Flow-Path Tracking Strategy in a Data-Driven Interwell Numerical Simulation Model for Waterflooding History Matching and Performance Prediction with Infill Wells

SPE Journal ◽

10.2118/199361-pa ◽

2019 ◽

Vol 25 (02) ◽

pp. 1007-1025 ◽

Cited By ~ 4

Author(s):

Hui Zhao ◽

Lingfei Xu ◽

Zhenyu Guo ◽

Qi Zhang ◽

Wei Liu ◽

...

Keyword(s):

Numerical Simulation ◽

Simulation Model ◽

History Matching ◽

Flow Path ◽

Path Tracking ◽

Data Driven ◽

Dynamic Allocation ◽

Field Problem ◽

Saturation Field ◽

And Control

Summary Recently, we have developed two computationally efficient data-driven models—interwell numerical simulation model (INSIM) and INSIM with front tracking (INSIM-FT)—for history matching, prediction, characterization, and optimization of waterflooding reservoirs. Then, stemming from the INSIM family, we derived a new data-driven model referred to as INSIM with flow-path tracking (FPT), for more-accurate interwell connectivity calculations, dynamic flow-path tracking, and waterflooding predictions. The model is a connection-based simulation model that is developed on the basis of a two-phase-flow material-balance equation. With the new model, we can characterize a reservoir by history matching the historical well flow-rate data without the detailed petrophysical properties of the reservoir. In INSIM-FPT, we provide an automatic and systematic workflow that incorporates Delaunay triangulation and imaginary wells to construct the model connection map. We apply a modified depth-first searching method to track all influential flow paths between an injector/producer pair for more-accurate calculations of dynamic allocation factors and control pore volumes (PVs). In addition, we provide a method to visualize a saturation field for a history-matched INSIM-FPT model. On the basis of the saturation map, we design a workflow to evaluate possible drilling locations and future performance of infill wells. For application, we create a synthetic reservoir with two different scenarios to demonstrate the reliability of INSIM-FPT. The results show that the dynamic allocation factors and control PVs between injector/producer pairs in the history-matched INSIM-FPT models are consistent with those obtained from the true streamline simulation model. Furthermore, the oil-saturation field generated with INSIM-FPT reasonably matches that obtained with the true model. It shows that the future predictions of infill wells on the basis of history-matched INSIM-FPT models are reasonably accurate but can be improved if more observed data are collected from near the planned infill wells. We also test a large-scale field problem with 65 wells, which shows INSIM-FPT can reasonably match and predict the field data.

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Analysis of Production Data from Communicating Multifractured Horizontal Wells Using the Dynamic Drainage Area Concept

SPE Reservoir Evaluation & Engineering ◽

10.2118/199987-pa ◽

2021 ◽

pp. 1-19

Author(s):

Hossein Ahmadi ◽

Christopher R. Clarkson ◽

Hamidreza Hamdi ◽

Hamid Behmanesh

Keyword(s):

Numerical Simulation ◽

Simulation Model ◽

Hydraulic Fracture ◽

History Matching ◽

Horizontal Wells ◽

Drainage Area ◽

Production Data ◽

Reservoir Properties ◽

Region Model ◽

Fluid Transfer

Summary Reduction of fracture/well spacing and increases in hydraulic fracture stimulation treatment size are popular strategies for improving hydrocarbon recovery from multifractured horizontal wells (MFHWs). However, these strategies can also increase the chance of fracture interference, which can not only negatively impact the overall production but also introduce complexities for production data analysis. A semianalytical model is therefore developed to analyze production data from two communicating MFHWs and applied to a field case. The new semianalytical model uses the dynamic drainage area (DDA) concept and assumes three porosity regions. The three-region model is comprised of a primary hydraulic fracture (PHF), an enhanced fractured region (EFR) adjacent to the PHF, and a nonstimulated region (NSR). Assuming a well pair primarily communicates through PHFs, the equations for two communicating wells are coupled and solved simultaneously to model the fluid transfer between the wells. This method is used within a history-matching framework to estimate the communication between the wells by matching the production data. The semianalytical model is first verified against a more rigorous, fully numerical simulation model for a range of fracture/reservoir properties. These comparisons demonstrate that there is excellent agreement between the fully numerical simulation model results and the new semianalytical model. The semianalytical model is then employed to history-match production data from six MFHWs (drilled from two adjacent well pads) exhibiting different degrees of communication. For the purpose of history matching the data, only strong communication between pairs of wells (intrapair communication) is considered in the three-region model, and the results show good agreement with the field data. A flexible, yet simple, semianalytical model is developed for the first time that can accurately model the communication between multiple well pairs. This approach can be used by reservoir engineers to analyze the production data from communicating MFHWs.

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

Frontiers in Earth Science ◽

10.3389/feart.2021.726537 ◽

2021 ◽

Vol 9 ◽

Author(s):

Huijun Wang ◽

Lu Qiao ◽

Shuangfang Lu ◽

Fangwen Chen ◽

Zhixiong Fang ◽

...

Keyword(s):

Numerical Simulation ◽

Simulation Model ◽

Shale Gas ◽

Horizontal Well ◽

Gas Production ◽

Production Model ◽

Support Vector ◽

Gas Production Prediction ◽

Half Length ◽

Production Prediction

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.

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Production Splitting Method for Commingled Production Reservoir Based on Automatic History Matching of Single Well

Lithosphere ◽

10.2113/2022/9225890 ◽

2022 ◽

Vol 2022 (Special 1) ◽

Author(s):

Yingfei Sui ◽

Chuanzhi Cui ◽

Zhen Wang ◽

Yong Yang ◽

Peifeng Jia

Keyword(s):

Numerical Simulation ◽

History Matching ◽

Splitting Method ◽

Production Performance ◽

Water Flooding ◽

Production Model ◽

Mitigation Measures ◽

Automatic History Matching ◽

Well Production ◽

Single Well

Abstract The interlayer interference is very serious in the process of water flooding development, especially when the reservoir adopts commingling production. The implementation of various interlayer interference mitigation measures requires that the production performance parameters and remaining oil distribution of each layer of the reservoir should be clearly defined, and the accurate production splitting of oil wells is the key. In this paper, the five-spot pattern is simplified to a single well production model of commingled production centered on oil well. The accurate production splitting results are obtained through automatic history matching of single well production performance. The comparison between the calculation results of this method and that of reservoir numerical simulation shows that the method is simple, accurate, and practical. In the field application, for the multilayer commingled production reservoir without accurate numerical simulation, this method can quickly and accurately realize the production splitting of the reservoir according to the development performance data.

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Identification and Impacts of Dual Strings Completion Leak in History Matching of Highly Faulted and Multi Layered Clastic Oil Reservoirs

10.2523/iptc-21856-ms ◽

2021 ◽

Author(s):

Nis Ilyani Mohmad ◽

Danu Ismadi ◽

Nor Hajjar Salleh ◽

Amirul Nur Romle ◽

Syarifah Puteh Syed Abd Rahim ◽

...

Keyword(s):

Numerical Simulation ◽

Oil Recovery ◽

History Matching ◽

Production Performance ◽

Model Parameters ◽

Reservoir Model ◽

Reservoir Performance ◽

Tool Evaluation ◽

Logging Tool

Abstract History matching is one of the paramount steps in reservoir model validation to describe, analyze and mimic the overall behavior of reservoir performance. Performing history matching on highly faulted and multi layered reservoirs is always challenging, especially when the wells are completed with multiple zones either with single selective or dual strings. The history matching complexity is also compounded with uncertainties in production allocation, well history and downhole equipment integrity overtime. It is a common practice for deterministic history matching in reservoir numerical simulation to modify the both static and dynamic model parameters within the subsurface uncertainty window. However, for multi layered reservoirs completed with dual strings, another parameter that is most often get overlooked is the completion string’s leaking phenomenon that tremendously impacting the history matching. The objective of this paper is to introduce dual strings leaking diagnostics methodology from various disciplines’ angles. We demonstrate these dual strings leaking phenomenon impact on history matching. This paper covers dual strings leak diagnostic methodology which includes production logging tool evaluation, well’s production performance and recovery factor analysis. Possible factors that gives rise to the string’s leaks including material corrosion from high CO2 and sand production will also be discussed. We will demonstrate on how the leak phenomenon could be mimicked in the reservoir numerical model. Possible risks on future infill well identification if the leaks phenomenon is not incorporated will be also discussed. The dual strings leaks diagnosis and application in numerical simulation is illustrated on a case study of Field "D", a multilayered sandstone reservoir in Malaysia of almost 3 decades of production. This proven leak identification and reservoir model history matching methodology has been replicated for all the fault blocks across the field. It potentially unlocks more than 100 MMSTB of additional oil recovery by drilling more oil producers and water injectors in future drilling campaigns.

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