Fast History Matching and Optimization Using a Novel Physics-Based Data-Driven Model: An Application to a Diatomite Reservoir

2021 ◽  
Author(s):  
Zhenzhen Wang ◽  
Jincong He ◽  
William J. Milliken ◽  
Xian-Huan Wen
Keyword(s):  
Capillary Pressure ◽  
Network Model ◽  
History Matching ◽  
Net Present Value ◽  
Analytic Solutions ◽  
Data Driven ◽  
Production Data ◽  
Well Control ◽  
Compositional Flow ◽  
Control Optimization

Abstract Full-physics models in history matching and optimization can be computationally expensive since these problems usually require hundreds of simulations or more. We have previously implemented a physics-based data-driven network model with a commercial simulator that serves as a surrogate without the need to build the 3-D geological model. In this paper, we reconstruct the network model to account for complex reservoir conditions of mature fields and successfully apply it to a diatomite reservoir in the San Joaquin Valley (SJV) for rapid history matching and optimization. The reservoir is simplified into a network of 1-D connections between well perforations. These connections are discretized into grid blocks and the grid properties are calibrated to historical production data. Elevation change, saturation distribution, capillary pressure, and relative permeability are accounted for to best represent the mature field conditions. To simulate this physics-based network model through a commercial simulator, an equivalent 2-D Cartesian model is designed where rows correspond to the above-mentioned connections. Thereafter, the history matching can be performed with the Ensemble Smoother with Multiple Data Assimilation (ESMDA) algorithm under a sequential iterative process. A representative model after history matching is then employed for well control optimization. The network model methodology has been successfully applied to the waterflood optimization for a 56-well sector model of a diatomite reservoir in the SJV. History matching result shows that the network model honors field-level production history and gives reasonable matches for most of the wells, including pressure and flow rate. The calibrated ensemble from the last iteration of history matching yields a satisfactory production prediction, which is verified by the remaining historical data. For well control optimization, we select the P50 model to maximize the Net Present Value (NPV) in 5 years under provided well/field constraints. This confirms that the calibrated network model is accurate enough for production forecasts and optimization. The use of a commercial simulator in the network model provided flexibility to account for complex physics, such as elevation difference between wells, saturation non-equilibrium, and strong capillary pressure. Unlike traditional big-loop workflow that relies on a detailed characterization of geological models, the proposed network model only requires production data and can be built and updated rapidly. The model also runs much faster (tens of seconds) than a full-physics model due to the employment of much fewer grid blocks. To our knowledge, this is the first time this physics-based data-driven network model is applied with a commercial simulator on a field waterflood case. Unlike approaches developed with analytic solutions, the use of commercial simulator makes it feasible to be further extended for complex processes, e.g., thermal or compositional flow. It serves as an useful surrogate model for both fast and reliable decision-making in reservoir management.

Fast History Matching and Optimization Using a Novel Physics-BasedData-Driven Model: An Application to a Diatomite Reservoir

SPE Journal ◽  
2021 ◽  
pp. 1-20
Author(s):  
Z. Wang ◽  
J. He ◽  
W. J. Milliken ◽  
X. -H. Wen
Keyword(s):  
Capillary Pressure ◽  
Network Model ◽  
History Matching ◽  
Net Present Value ◽  
Analytic Solutions ◽  
San Joaquin Valley ◽  
Production Data ◽  
Well Control ◽  
Compositional Flow ◽  
Control Optimization

Summary Full-physics models in history matching (HM) and optimization can be computationally expensive because these problems usually require hundreds of simulations or more. In a previous study, a physics-baseddata-driven network model was implemented with a commercial simulator that served as a surrogate without the need to build a 3D geological model. In this paper, the network model is reconstructed to account for complex reservoir conditions of mature fields and successfully apply it to a diatomite reservoir in the San Joaquin Valley, California, for rapid HM and optimization. The reservoir is simplified into a network of 1D connections between well perforations. These connections are discretized into gridblocks, and the grid properties are calibrated to historical production data. Elevation change, saturation distribution, capillary pressure, and relative permeability are accounted for to best represent the mature field conditions. To simulate this physics-based network model through a commercial simulator, an equivalent Cartesian model is designed where rows correspond to the previously mentioned connections. Thereafter, the HM can be performed with the ensemble smoother with multiple data assimilation (ESMDA) algorithm under a sequential iterative process. A representative model after HM is then used for well control optimization. The network model methodology has been successfully applied to the waterflood optimization for a 56-well sector model of a diatomite reservoir in the San Joaquin Valley. HM results show that the network model matches with field level production history and gives reasonable matches for most of the wells, including pressure and volumetric data. The calibrated posterior ensemble of HM yields a satisfactory production prediction that is verified by the remaining historical data. For well control optimization, the P50 model is selected to maximize the net present value (NPV) in 5 years under provided well/field constraints. This confirms that the calibrated network model is accurate enough for production forecasts and optimization. The use of a commercial simulator in the network model provided flexibility to account for complex physics, such as elevation difference between wells, saturation nonequilibrium, and strong capillary pressure. Unlike the traditional big-loop workflow that relies on a detailed characterization of geological models, the proposed network model only requires production data and can be built and updated rapidly. The model also runs much faster (tens of seconds) than a full-physics model because of the use of much fewer gridblocks. To our knowledge, this is the first time this physics-baseddata-driven network model is applied with a commercial simulator on a field waterflood case. Unlike approaches developed with analytic solutions, the use of a commercial simulator makes it feasible to be further extended for complex processes (e.g., thermal or compositional flow). It serves as a useful surrogate model for both fast and reliable decision-making in reservoir management.

scholarly journals Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization

2021 ◽  
Author(s):  
Cuthbert Shang Wui Ng ◽  
Ashkan Jahanbani Ghahfarokhi ◽  
Menad Nait Amar
Keyword(s):  
Case Studies ◽  
Net Present Value ◽  
Computational Cost ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Reservoir Model ◽  
Well Control ◽  
Optimization Task ◽  
Proxy Models ◽  
Artificial Neural

AbstractWith the aid of machine learning method, namely artificial neural networks, we established data-driven proxy models that could be utilized to maximize the net present value of a waterflooding process by adjusting the well control injection rates over a production period. These data-driven proxies were maneuvered on two different case studies, which included a synthetic 2D reservoir model and a 3D reservoir model (the Egg Model). Regarding the algorithms, we applied two different nature-inspired metaheuristic algorithms, i.e., particle swarm optimization and grey wolf optimization, to perform the optimization task. Pertaining to the development of the proxy models, we demonstrated that the training and blind validation results were excellent (with coefficient of determination, R2 being about 0.99). For both case studies and the optimization algorithms employed, the optimization results obtained using the proxy models were all within 5% error (satisfied level of accuracy) compared with reservoir simulator. These results confirm the usefulness of the methodology in developing the proxy models. Besides that, the computational cost of optimization was significantly reduced using the proxies. This further highlights the significant benefits of employing the proxy models for practical use despite being subject to a few constraints.

A Physics-Based Data-Driven Model for History Matching, Prediction, and Characterization of Waterflooding Performance

SPE Journal ◽  
2017 ◽  
Vol 23 (02) ◽  
pp. 367-395 ◽  
Author(s):  
Zhenyu Guo ◽  
Albert C. Reynolds ◽  
Hui Zhao
Keyword(s):  
History Matching ◽  
Ad Hoc ◽  
Front Tracking ◽  
Original Data ◽  
Substantial Improvement ◽  
Data Driven ◽  
Production Data ◽  
New Model ◽  
Front Tracking Method ◽  
Reservoir Performance

Summary We develop and use a new data-driven model for assisted history matching of production data from a reservoir under waterflood and apply the history-matched model to predict future reservoir performance. Although the model is developed from production data and requires no prior knowledge of rock-property fields, it incorporates far more fundamental physics than that of the popular capacitance–resistance model (CRM). The new model also represents a substantial improvement on an interwell-numerical-simulation model (INSIM) that was presented previously in a paper coauthored by the latter two authors of the current paper. The new model, which is referred to as INSIM-FT, eliminates the three deficiencies of the original data-driven INSIM. The new model uses more interwell connections than INSIM to increase the fidelity of history matching and predictions and replaces the ad hoc computation procedure for computing saturation that is used in INSIM by a theoretically sound front-tracking procedure. Because of the introduction of a front-tracking method for the calculation of saturation, the new model is referred to as INSIM-FT. We compare the performance of CRM, INSIM, and INSIM-FT in two synthetic examples. INSIM-FT is also tested in a field example.

scholarly journals Stochastic optimization strategies applied to the OLYMPUS benchmark

2019 ◽  
Vol 24 (6) ◽  
pp. 1943-1958 ◽  
Author(s):  
V. L. S. Silva ◽  
M. A. Cardoso ◽  
D. F. B. Oliveira ◽  
R. J. de Moraes
Keyword(s):  
Stochastic Optimization ◽  
Net Present Value ◽  
Computational Cost ◽  
Real Life ◽  
Sequential Optimization ◽  
Optimization Approach ◽  
Well Placement ◽  
Well Control ◽  
Field Development ◽  
Control Optimization

AbstractIn this work, we discuss the application of stochastic optimization approaches to the OLYMPUS case, a benchmark challenge which seeks the evaluation of different techniques applied to well control and field development optimization. For that matter, three exercises have been proposed, namely, (i) well control optimization; (ii) field development optimization; and (iii) joint optimization. All applications were performed considering the so-called OLYMPUS case, a synthetic reservoir model with geological uncertainty provided by TNO (Fonseca 2018). Firstly, in the well control exercise, we successfully applied an ensemble-based approximate gradient method in a robust optimization formulation. Secondly, we solve the field development exercise using a genetic algorithm framework designed with special features for the problem of interest. Finally, in order to evaluate further gains, a sequential optimization approach was employed, in which we run one more well control optimization based on the optimal well locations. Even though we utilize relatively well-known techniques in our studies, we describe the necessary adaptations to the algorithms that enable their successful applications to real-life scenarios. Significant gains in the expected net present value are obtained: in exercise (i) a gain of 7% with respect to reactive control; for exercise (ii) a gain of 660% with respect to a initial well placement based on an engineering approach; and for (iii) an extra gain of 3% due to an additional well control optimization after the well placement optimization. All these gains are obtained with an affordable computational cost via the extensive utilization of high-performance computing (HPC) infrastructure. We also apply a scenario reduction technique to exercise (i), with similar gains obtained in the full ensemble optimization, however, with substantially inferior computational cost. In conclusion, we demonstrate how the state-of-the-art optimization technology available in the model-based reservoir management literature can be successfully applied to field development optimization via the conscious utilization of HPC facilities.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals Wettability Effects on Oil-Recovery Mechanisms in Fractured Reservoirs

2001 ◽  
Vol 4 (06) ◽  
pp. 455-466 ◽  
Author(s):  
A. Graue ◽  
T. Bognø ◽  
B.A. Baldwin ◽  
E.A. Spinler
Keyword(s):  
Crude Oil ◽  
Numerical Simulations ◽  
Capillary Pressure ◽  
Oil Recovery ◽  
History Matching ◽  
Water Saturation ◽  
Fracture Network ◽  
Stock Tank ◽  
Recovery Mechanisms

Summary Iterative comparison between experimental work and numerical simulations has been used to predict oil-recovery mechanisms in fractured chalk as a function of wettability. Selective and reproducible alteration of wettability by aging in crude oil at an elevated temperature produced chalk blocks that were strongly water-wet and moderately water-wet, but with identical mineralogy and pore geometry. Large scale, nuclear-tracer, 2D-imaging experiments monitored the waterflooding of these blocks of chalk, first whole, then fractured. This data provided in-situ fluid saturations for validating numerical simulations and evaluating capillary pressure- and relative permeability-input data used in the simulations. Capillary pressure and relative permeabilities at each wettability condition were measured experimentally and used as input for the simulations. Optimization of either Pc-data or kr-curves gave indications of the validity of these input data. History matching both the production profile and the in-situ saturation distribution development gave higher confidence in the simulations than matching production profiles only. Introduction Laboratory waterflood experiments, with larger blocks of fractured chalk where the advancing waterfront has been imaged by a nuclear tracer technique, showed that changing the wettability conditions from strongly water-wet to moderately water-wet had minor impact on the the oil-production profiles.1–3 The in-situ saturation development, however, was significantly different, indicating differences in oil-recovery mechanisms.4 The main objective for the current experiments was to determine the oil-recovery mechanisms at different wettability conditions. We have reported earlier on a technique that reproducibly alters wettability in outcrop chalk by aging the rock material in stock-tank crude oil at an elevated temperature for a selected period of time.5 After applying this aging technique to several blocks of chalk, we imaged waterfloods on blocks of outcrop chalk at different wettability conditions, first as a whole block, then when the blocks were fractured and reassembled. Earlier work reported experiments using an embedded fracture network,4,6,7 while this work also studied an interconnected fracture network. A secondary objective of these experiments was to validate a full-field numerical simulator for prediction of the oil production and the in-situ saturation dynamics for the waterfloods. In this process, the validity of the experimentally measured capillary pressure and relative permeability data, used as input for the simulator, has been tested at strongly water-wet and moderately water-wet conditions. Optimization of either Pc data or kr curves for the chalk matrix in the numerical simulations of the whole blocks at different wettabilities gave indications of the data's validity. History matching both the production profile and the in-situ saturation distribution development gave higher confidence in the simulations of the fractured blocks, in which only the fracture representation was a variable. Experimental Rock Material and Preparation. Two chalk blocks, CHP8 and CHP9, approximately 20×12×5 cm thick, were obtained from large pieces of Rørdal outcrop chalk from the Portland quarry near Ålborg, Denmark. The blocks were cut to size with a band saw and used without cleaning. Local air permeability was measured at each intersection of a 1×1-cm grid on both sides of the blocks with a minipermeameter. The measurements indicated homogeneous blocks on a centimeter scale. This chalk material had never been contacted by oil and was strongly water-wet. The blocks were dried in a 90°C oven for 3 days. End pieces were mounted on each block, and the whole assembly was epoxy coated. Each end piece contained three fittings so that entering and exiting fluids were evenly distributed with respect to height. The blocks were vacuum evacuated and saturated with brine containing 5 wt% NaCl+3.8 wt% CaCl2. Fluid data are found in Table 1. Porosity was determined from weight measurements, and the permeability was measured across the epoxy-coated blocks, at 2×10–3 µm2 and 4×10–3 µm2, for CHP8 and CHP9, respectively (see block data in Table 2). Immobile water saturations of 27 to 35% pore volume (PV) were established for both blocks by oilflooding. To obtain uniform initial water saturation, Swi, oil was injected alternately at both ends. Oilfloods of the epoxy-coated block, CHP8, were carried out with stock-tank crude oil in a heated pressure vessel at 90°C with a maximum differential pressure of 135 kPa/cm. CHP9 was oilflooded with decane at room temperature. Wettability Alteration. Selective and reproducible alteration of wettability, by aging in crude oil at elevated temperatures, produced a moderately water-wet chalk block, CHP8, with similar mineralogy and pore geometry to the untreated strongly water-wet chalk block CHP9. Block CHP8 was aged in crude oil at 90°C for 83 days at an immobile water saturation of 28% PV. A North Sea crude oil, filtered at 90°C through a chalk core, was used to oilflood the block and to determine the aging process. Two twin samples drilled from the same chunk of chalk as the cut block were treated similar to the block. An Amott-Harvey test was performed on these samples to indicate the wettability conditions after aging.8 After the waterfloods were terminated, four core plugs were drilled out of each block, and wettability measurements were conducted with the Amott-Harvey test. Because of possible wax problems with the North Sea crude oil used for aging, decane was used as the oil phase during the waterfloods, which were performed at room temperature. After the aging was completed for CHP8, the crude oil was flushed out with decahydronaphthalene (decalin), which again was flushed out with n-decane, all at 90°C. Decalin was used as a buffer between the decane and the crude oil to avoid asphalthene precipitation, which may occur when decane contacts the crude oil.

Streamline Tracing and Applications in Dual Porosity Dual Permeability Models

2021 ◽  
Author(s):  
Tsubasa Onishi ◽  
Hongquan Chen ◽  
Jiang Xie ◽  
Shusei Tanaka ◽  
Dongjae Kam ◽  
...  
Keyword(s):  
History Matching ◽  
Net Present Value ◽  
Time Of Flight ◽  
Rate Allocation ◽  
Production Optimization ◽  
Dual Porosity ◽  
Computationally Efficient ◽  
Present Value ◽  
Sweep Efficiency ◽  
Streamline Tracing

Abstract Streamline-based methods have proven to be effective for various subsurface flow and transport modeling problems. However, the applications are limited in dual-porosity and dual-permeability (DPDK) system due to the difficulty in describing interactions between matrix and fracture during streamline tracing. In this work, we present a robust streamline tracing algorithm for DPDK models and apply the new algorithm to rate allocation optimization in a waterflood reservoir. In the proposed method, streamlines are traced in both fracture and matrix domains. The inter-fluxes between fracture and matrix are described by switching streamlines from one domain to another using a probability computed based on the inter-fluxes. The approach is fundamentally similar to the existing streamline tracing technique and can be utilized in streamline-assisted applications, such as flow diagnostics, history matching, and production optimization. The proposed method is benchmarked with a finite-volume based approach where grid-based time-of-flight was obtained by solving the stationary transport equation. We first validated our method using simple examples. Visual time-of-flight comparisons as well as tracer concentration and allocation factors at wells show good agreement. Next, we applied the proposed method to field scale models to demonstrate the robustness. The results show that our method offers reduced numerical artifacts and better represents reservoir heterogeneity and well connectivity with sub-grid resolutions. The proposed method is then used for rate allocation optimization in DPDK models. A streamline-based gradient free algorithm is used to optimize net present value by adjusting both injection and production well rates under operational constraints. The results show that the optimized schedule offers significant improvement in recovery factor, net present value, and sweep efficiency compared to the base scenario using equal rate injection and production. The optimization algorithm is computationally efficient as it requires only a few forward reservoir simulations.

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