Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization

Ahmadi, Mohammad Ali; Zendehboudi, Sohrab; Lohi, Ali; Elkamel, Ali; Chatzis, Ioannis

doi:10.1111/j.1365-2478.2012.01080.x

Cited by 197 publications

(79 citation statements)

References 29 publications

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“…(6) in the nonlinear curve fitting function in OriginLab (Origin Pro 8.5) based on 410 measured results. The fitting curve of these four variables is obtained with a R 2 of 0.96, which shows a very satisfactory performance for the prediction formula (Coulibaly and Baldwin 2005;Ali Ahmadi et al 2013):…”

Section: Prediction Formulamentioning

confidence: 99%

A prediction formula for ratio of injection–production control area in triangle well pattern

Chen

Feng

Zhang

et al. 2017

J Petrol Explor Prod Technol

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The ratio of injection-production control area is an important aspect in evaluating the development effect and calculating oilfield development index. Conventional approaches to determine the ratio of injection-production control area neglect the heterogeneity of the reservoir and require long time simulating and a lot of complex iterations. This paper establishes a prediction formula to quickly determine the ratio of injection-production control area in triangle well pattern. A total of 410 sets of streamline models are built to acquire the database. Permeability, oil saturation, injection-production pressure drop and injector-producer spacing are selected as independent variables to establish the prediction formula by multivariate parametric regression. Based on error analysis and application, the accuracy of this prediction formula is approved. Results indicate that the prediction formula has a correlation coefficient R 2 of 0.96, representing a satisfactory performance. Normality and homoscedasticity tests and standardized residual diagnostics demonstrate the statistical significance of the results. The application of this prediction formula shows an excellent match between the predicted and actual injection-production area, which further confirms the accuracy of this prediction formula. The established prediction formula can effectively and accurately decide the ratio of injection-production control area for waterflooding reservoir.

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Section: Prediction Formulamentioning

confidence: 99%

A prediction formula for ratio of injection–production control area in triangle well pattern

Chen

Feng

Zhang

et al. 2017

J Petrol Explor Prod Technol

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show abstract

“…Location of the Mansouri oilfield on the map and position of the wells used in this study have shown on top of the Asmari formation (figure 2). Asmari is an asymmetric anticline dipping around 0-10 deg at both ends (Ahmadi et al, 2012). A comprehensive study by Van Buchem et al (2010) indicated that the OligoceneMiocene Asmari formation mostly consists of shallow-water carbonate depositions and siliciclastic.…”

Section: Geological Settingsmentioning

confidence: 99%

“…It follows the northwest-southeast trend of Zagros, with the length of 30 km and width of 5 km. It consists of two major reservoirs namely Asmari and Bangestan (Ahmadi et al, 2012). Investigating lateral lithology variations, complex stratigraphy, and pore pressure of the Asmari formation throughout the basin is the main focus of this study and a major challenge for drilling companies.…”

Section: Geological Settingsmentioning

confidence: 99%

Enhanced velocity based pore pressure prediction using lithofacies clustering: A case study from a reservoir with complex lithology in Dezful Embayment, SW Iran

Arabameri¹,

soleymani²,

Tokhmechi³

2018

Preprint

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Velocity based pore pressure prediction methods are widely accepted as a routine technique in the petroleum industry. Despite recent improvements, still, literature suffer from inconsistencies and uncertainties mostly arise from velocity anomalies due to complex lithostratigraphic setting or presence of various formation fluids. The primary goal of this paper is to improve the accuracy and reliability of the conventional Bowers and Tau methods in a reservoir with complex lithology. Our proposed workflow aims to improve the accuracy of the estimations by clustering the input data based on specific petrophysical characteristics. We show since each major zones at the offset test wells have a distinct compaction trend, empirical constants in Bowers and Tau methods can be calibrated for each cluster rather than the whole stratigraphic column. The clustering task was done by statistical analyses of a suite of well logs and validated with core derived lithologies. To find the best clustering algorithm, we applied and compared five techniques namely, K-means, basic sequential algorithmic scheme, single, and complete linkage hierarchical. We found that the self-organizing map (SOM) method provides the best results by maximizing lithology likelihood within each cluster and improve the overall accuracy of the Bowers and Tau methods. This research also aims to provide a systematic comparison of the mentioned clustering algorithms based on their ability in distinguishing various lithofacies. We also try to minimize the user interference in the process of clustering multiple lithofacies and improve the reproducibility of the results and demonstrate the capability of the proposed method through a case study in a reservoir in the Southwest of Iran. Satisfactory results of this study offer a safe ground for implementation of the proposed method in other sedimentary basins.

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“…There are several other hybrid algorithm examples available in the literature: a hybrid PSO and GA [192]; hybrid GA and DE [193]; hybrid PSO and GSO [194]; and hybrid PSO and optimal foraging theory [195].…”

Section: Weight Optimizationmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

414

154

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Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization

Cited by 197 publications

References 29 publications

A prediction formula for ratio of injection–production control area in triangle well pattern

A prediction formula for ratio of injection–production control area in triangle well pattern

Enhanced velocity based pore pressure prediction using lithofacies clustering: A case study from a reservoir with complex lithology in Dezful Embayment, SW Iran

Metaheuristic design of feedforward neural networks: A review of two decades of research

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