Automatic History Matching for an Integrated Reservoir Description and 
Improving Oil Recovery

Sultan, A.J.; Ouenes, Ahmed; Weiss, W.W.

doi:10.2523/27712-ms

Cited by 10 publications

(6 citation statements)

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“…the geological properties influencing fluid flow. Sultan et al (1994) and Ouenes et al (1993) used the Simulated Annealing Method (SAM) to automate history matching process. SAM was a non-gradient optimization method capable of handling large number of parameters.…”

Section: History Matchingmentioning

confidence: 99%

Artificial Intelligence (AI) Assisted History Matching

et al. 2014

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History matching is the process of adjusting uncertain reservoir parameters until an acceptable match with the measured production data is obtained. Complexity and insufficient knowledge of reservoir characteristics makes this process timeconsuming with high computational cost. In the recent years, many efforts mainly referred as assisted history matching have attempted to make this process faster; nevertheless, the degree of success of these techniques continues to be a subject for debate.This study aims to examine the application of a unique pattern recognition technology to improve the time and efforts required for completing a successful history matching project. The pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM) are used to develop Surrogate Reservoir Model (SRM) for utilization as the engine to drive the history matching process. SRM is an intelligent prototype of the full-field reservoir simulation model that runs in fractions of a second. SRM is built using a handful of geological realizations.In this study, a synthetic reservoir model of a heterogeneous oilfield with 24 production wells and 30 years of production history was used as the ground truth (the subject and the goal of the history match). An SRM was created to accurately represent this reservoir model. The history matching process for this field was performed using the SRM and by tuning static data (Permeability). The result of this study demonstrates the capabilities of SRM for fast track and accurate reproduction of the numerical model results. Speed and accuracy make SRM a fast and effective tool for assisted history matching.

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Section: History Matchingmentioning

confidence: 99%

Artificial Intelligence (AI) Assisted History Matching

et al. 2014

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“…In general, these algorithms can be categorized into two groups: (1) gradient-based methods such as the Gaussian-Newton method (Thomas et al 1972) and the Levenberg-Marquardt algorithm (Reynolds et al 2004); and (2) gradient-free methods including GAs (Castellini et al 2005) and simulated annealing (SA) algorithms (Sultan et al 1994). In order to obtain the gradient search direction, the gradient of the objective function is required, and it can be obtained by using an adjoint equation (Li et al 2001) or by computation of the sensitivity coefficient (Tan and Kalogerakis 1992).…”

Section: Introductionmentioning

confidence: 99%

Application of Genetic Algorithm (GA) in History Matching of the Vapour Extraction (VAPEX) Heavy Oil Recovery Process

Zhang

Zeng

et al. 2014

Nat Resour Res

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This paper presents the application of genetic algorithm (GA) to the history-matching problem. As history matching of VAPEX (vapour extraction) experiments is a complex, highly nonlinear, and non-unique inverse problem, a modified GA was developed to assist the history-matching process. Compared to conventional GA, the computational time in this modified GA approach was reduced by 71 %, and an excellent match between the simulation data and experimental data was achieved, with the error being less than 1 %. This study is focussed on automatic history matching of the VAPEX heavy oil recovery process.

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“…Gradients based methods showed less performance in history matching because of their tendency to get trapped in local minima. In stochastic methods, many authors have tried different algorithms like simulated annealing (SA) (Sultan et al, 1994), neighbourhood algorithm (NA) (Subbey et al, 2003), genetic algorithms (GA) (Castellini et al, 2005;Sangwai et al, 2007), scatter search (SS) (Sousa et al, 2006;Erbas and Christie, 2007), Markov chain Monte Carlo (McMC) (Maucec et al, 2007), particle swarm optimization (PSO) (Mohamed et al, 2009), ant colony optimization (Hajizadeh et al, 2011), differential evolution (DE) (Hajizadeh et al, 2010). It is observed that the differential evolution algorithm has shown good results for the history matching but the performance of the algorithm was very much sensitive to the value of control parameters such as crossover rate.…”

Section: Introductionmentioning

confidence: 99%