Method for Automatic History Matching of Reservoir Simulation Models

1973 ◽  
Author(s):  
Luzbel Napoleon Solorzano ◽  
S.E. Arredondo ◽  
Enrique Sergio
Keyword(s):  
Reservoir Simulation ◽  
History Matching ◽  
Simulation Models ◽  
Automatic History Matching

Hybrid Fluid Flow Simulation Combining Full Physics Simulation and Artificial Intelligence

2021 ◽  
Author(s):  
Mokhles Mezghani ◽  
Mustafa AlIbrahim ◽  
Majdi Baddourah
Keyword(s):  
Artificial Intelligence ◽  
Reservoir Simulation ◽  
History Matching ◽  
Hybrid Approach ◽  
Flow Simulation ◽  
Latin Hypercube Sampling ◽  
Simulation Models ◽  
Fine Scale ◽  
Simulation Results ◽  
Optimization Loop

Abstract Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.

A Comparison Study on Algorithms in Reservoir Automatic History Matching

2012 ◽  
Vol 518-523 ◽  
pp. 4376-4379
Author(s):  
Bao Yi Jiang ◽  
Zhi Ping Li
Keyword(s):  
Objective Function ◽  
Reservoir Simulation ◽  
History Matching ◽  
Optimization Algorithms ◽  
Reservoir Engineering ◽  
Comparison Study ◽  
Automatic History Matching ◽  
Numerical Reservoir Simulation ◽  
Computational Capability ◽  
Matching Procedure

With the increase in computational capability, numerical reservoir simulation has become an essential tool for reservoir engineering. To minimize the objective function involved in the history matching procedure, we need to apply the optimization algorithms. This paper is based on the optimization algorithms used in automatic history matching.

Uncertainty Analysis in the Production History Matching by Using Proxies as Reservoir Simulators

2012 ◽  
Author(s):  
Paulo Camargo Silva ◽  
Virgílio José Martins Ferreira Filho
Keyword(s):  
Probability Density ◽  
Reservoir Simulation ◽  
History Matching ◽  
Simulation Models ◽  
Difficult Problem ◽  
Probability Density Functions ◽  
Density Functions ◽  
Production History ◽  
Definition Of ◽  
Reservoir Simulator

In the recent literature of the production history matching the problem of non-uniqueness of reservoir simulation models has been considered a difficult problem. Complex workflows have been proposed to solve the problem. However, the reduction of uncertainty can only be done with the definition of Probability Density Functions that are highly costly. In this article we introduce a methodology to reduce uncertainty in the history matching using techniques of Monte Carlo performed on proxies as Reservoir Simulator. This methodology is able to compare different Probability Density Functions for different reservoir simulation models to define among the models which simulation model can provide more appropriate matching.

A Nonlinear Automatic History Matching Technique for Reservoir Simulation Models

1972 ◽  
Vol 12 (06) ◽  
pp. 508-514 ◽  
Author(s):  
L. Kent Thomas ◽  
L.J. Hellums ◽  
G.M. Reheis
Keyword(s):  
Linear Programming ◽  
Least Squares ◽  
Reservoir Simulation ◽  
History Matching ◽  
Nonlinear Problems ◽  
Performance Data ◽  
Automatic History Matching ◽  
Reservoir Simulations ◽  
Highly Nonlinear ◽  
Matching Procedure

Abstract This paper presents a nonlinear optimization technique that automatically varies reservoir parameters to obtain a history match of held parameters to obtain a history match of held performance. The method is based on the classical performance. The method is based on the classical Gauss-Newton least-squares procedure. The range of each parameter is restricted by a box-type constraint and special provisions are included to handle highly nonlinear cases. Any combination of reservoir parameters may be used as the optimization variables and any set or sets of held data may be included in the match. Several history matches are presented, including examples from previous papers for comparison. In each of these examples, the technique presented here resulted in equivalent history matches in as few or fewer simulation runs. Introduction The history matching phase of reservoir simulations usually requires a trial-and-error procedure of adjusting various reservoir parameters procedure of adjusting various reservoir parameters and then calculating field performance. This procedure is continued until an acceptable match procedure is continued until an acceptable match between field and calculated performance has been obtained and can become quite tedious and time consuming, even with a small number of reservoir parameters, because of the interaction between the parameters, because of the interaction between the parameters and calculated performance. parameters and calculated performance. Recently various automatic or semiautomatic history-matching techniques have been introduced. Jacquard and Jain presented a technique based on a version of the method of steepest descent. They did not consider their method to be fully operational, however, due to the lack of experience with convergence. Jahns presented a method based on the Gauss-Newton equation with a stepwise solution for speeding convergence; but his procedure still required a large number of reservoir simulations to proceed to a solution. Coats et al. presented a proceed to a solution. Coats et al. presented a workable automatic history-matching procedure based on least-squares and linear programming. The method presented by Slater and Durrer is based on a gradient method and linear programming. In their paper they mention the difficulty of choosing a step paper they mention the difficulty of choosing a step size for their gradient method, especially for problems involving low values of porosity and problems involving low values of porosity and permeability. They also point out the need for a permeability. They also point out the need for a fairly small range on their reservoir description parameters for highly nonlinear problems. Thus, parameters for highly nonlinear problems. Thus, work in this area to date has resulted either in techniques based on a linear parameter-error dependence or in nonlinear techniques which require a considerable number of simulation runs. The method presented here is a nonlinear algorithm that will match both linear and nonlinear systems in a reasonable number of simulations. HISTORY MATCHING In a reservoir simulation, various performance data for the field, such as well pressures, gas-oil ratios, and water-oil ratios, are used as the basis for the match. During the matching of these performance data certain reservoir and fluid performance data certain reservoir and fluid parameters are assumed to be known while other parameters are assumed to be known while other less reliable data, forming the set (x1, x2...xn), are varied to achieve a match. The objective of the history-matching procedure presented in this paper is to minimize, in a presented in this paper is to minimize, in a least-squares sense, the error between the set of observed and calculated performance data, Fk(x1, x2... xn). SPEJ P. 508

scholarly journals Refining Permeability Values for History-Matching of the Reservoir Simulation Model

2020 ◽  
Vol 20 (3) ◽  
pp. 223-230
Author(s):  
Margarita A. Smetkina ◽  
◽  
Oleg A. Melkishev ◽  
Maksim A. Prisyazhnyuk ◽  
◽  
...  
Keyword(s):  
Reservoir Simulation ◽  
History Matching ◽  
Simulation Models ◽  
Data Interpretation ◽  
Well Logging ◽  
Initial Permeability ◽  
Oil Field ◽  
Statistical Characteristics ◽  
Reservoir Model ◽  
Porosity And Permeability

Reservoir simulation models are used to design oil field developments, estimate efficiency of geological and engineering operations and perform prediction calculations of long-term development performances. A method has been developed to adjust the permeability cube values during reservoir model history-matching subject to the corederived dependence between rock petrophysical properties. The method was implemented using an example of the Bobrikovian formation (terrigenous reservoir) deposit of a field in the Solikamskian depression. A statistical analysis of the Bobrikovian formation porosity and permeability properties was conducted following the well logging results interpretation and reservoir modelling data. We analysed differences between the initial permeability obtained after upscaling the geological model and permeability obtained after the reservoir model history-matching. The analysis revealed divergences between the statistical characteristics of the permeability values based on the well logging data interpretation and the reservoir model, as well as substantial differences between the adjusted and initial permeability cubes. It was established that the initial permeability was significantly modified by manual adjustments in the process of history-matching. Extreme permeability values were defined and corrected based on the core-derived petrophysical dependence KPR = f(KP) , subject to ranges of porosity and permeability ratios. By using the modified permeability cube, calculations were performed to reproduce the formation production history. According to the calculation results, we achieved convergence with the actual data, while deviations were in line with the accuracy requirements to the model history-matching. Thus, this method of the permeability cube adjustment following the manual history-matching will save from the gross overestimation or underestimation of permeability in reservoir model cells.

History to Prediction Transition Calibration of Reservoir Simulation Model Ensemble

2021 ◽  
Author(s):  
Bjørn Egil Ludvigsen ◽  
Mohan Sharma
Keyword(s):  
Simulation Model ◽  
Reservoir Simulation ◽  
History Matching ◽  
Simulation Models ◽  
Productivity Index ◽  
Model Parameters ◽  
Gas Oil ◽  
Well Performance ◽  
Reservoir Simulation Model ◽  
Water Cut

Abstract Well performance calibration after history matching a reservoir simulation model ensures that the wells give realistic rates during the prediction phase. The calibration involves adjusting well model parameters to match observed production rates at specified backpressure(s). This process is usually very time consuming such that the traditional approaches using one reservoir model with hundreds of high productivity wells would take months to calibrate. The application of uncertainty-centric workflows for reservoir modeling and history matching results in many acceptable matches for phase rates and flowing bottom-hole pressure (BHP). This makes well calibration even more challenging for an ensemble of large number of simulation models, as the existing approaches are not scalable. It is known that Productivity Index (PI) integrates reservoir and well performance where most of the pressure drop happens in one to two grid blocks around well depending upon the model resolution. A workflow has been setup to fix transition by calibrating PI for each well in a history matched simulation model. Simulation PI can be modified by changing permeability-thickness (Kh), skin, or by applying PI multiplier as a correction. For a history matched ensemble with a range in water-cut and gas-oil ratio, the proposed workflow involves running flowing gradient calculations for a well corresponding to observed THP and simulated rates for different phases to calculate target BHP. A PI Multiplier is then calculated for that well and model that would shift simulation BHP to target BHP as local update to reduce the extent of jump. An ensemble of history matched models with a range in water-cut and gas-oil ratio have a variation in required BHPs unique to each case. With the well calibration performed correctly, the jump observed in rates while switching from history to prediction can be eliminated or significantly reduced. The prediction thus results in reliable rates if wells are run on pressure control and reliable plateau if the wells are run on group control. This reduces the risk of under/over-predicting ultimate hydrocarbon recovery from field and the project's cashflow. Also, this allows running sensitivities to backpressure, tubing design, and other equipment constraints to optimize reservoir performance and facilities design. The proposed workflow, which dynamically couple reservoir simulation and well performance modeling, takes a few seconds to run for a well, making it fit-for-purpose for a large ensemble of simulation models with a large number of wells.

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