Petroleum reservoir characterization with the aid of artificial neural networks

Mohaghegh, Shahab D.; Arefi, Reza; Ameri, S.; Aminiand, Khashayar; Nutter, R.S.

doi:10.1016/s0920-4105(96)00028-9

Cited by 143 publications

(42 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Over the last few decades, a number of studies have investigated the application of a range of decision support and artificial intelligence techniques and technologies in candidate well selection. These range from decision support systems using multivariate nonlinear regression [4][5][6] to neural networks [7][8][9][10], analytical hierarchy process (AHP) [11][12][13][14][15][16] and fuzzy logic [10,[17][18][19][20]. Each of these approaches carries with it a set of advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

et al. 2017

View full text Add to dashboard Cite

Abstract:The selection of appropriate wells for hydraulic fracturing is one of the most important decisions faced by oilfield engineers. It has significant implications for the future development of an oilfield in terms of its productivity and economics. In this study, we developed a fuzzy model for well selection that combines the major objective criteria with the subjective judgments of decision makers. This was done by fusing the analytic hierarchy process (AHP) method, grey theory and an advanced version of fuzzy logic theory (FLT). The AHP component was used to identify the relevant criteria involved in selecting wells for hydraulic fracturing. Grey theory was used to determine the relative importance of those criteria. Then a fuzzy expert system was applied to fuzzily process the aggregated inputs using a Type-2 fuzzy logic system. This undertakes approximate reasoning and generates recommendations for candidate wells. These techniques and technologies were hybridized by using an intercommunication job-sharing method that integrates human judgment. The proposed method was tested on data from an oilfield in Western China and finally the most appropriate candidate wells for hydraulic fracturing were ranked in order of their projected output after fracturing.

show abstract

Section: Introductionmentioning

confidence: 99%

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

et al. 2017

View full text Add to dashboard Cite

show abstract

“…These nonlinear features lead to relatively difficult for traditional research methods. In recent years, artificial neural network is widely used in solving complex nonlinear problems, the researchers also proposed some neural network prediction model based on the price of petroleum [12], such as Wong and Mohaghegh [13] using the ANN model in the petroleum storage, and achieved good results; Chaudhuri [14] using ANN algorithm established the petroleum purity appraisal model, and the forecast effect is better. Although the prediction effect of these models is better, the defects of BP algorithm, such as easy to fall into local optimization, make the model convergence speed and approximation is not very satisfactory.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm

Li¹

2017

PAMJ

View full text Add to dashboard Cite

Petroleum price are affected by some uncertainties and nonlinear factors, how to predict the price effectively is the focus of the present study. In this paper, a 3 layers back propagation artificial neural network model based on particle swarm optimization algorithm combined with chaos theory and self-adaptive weight strategy is developed, the model structure is 7-13-1, and used to predict the petroleum price. By comparing with the other models, it shows that the model proposed in this paper has good prediction performance, the prediction accuracy and correlations are better.

show abstract

“…Heterogeneity in evaluating reservoir is referred to as nonlinear and non-uniform spatial distribution of rock properties such as porosity, permeability and fluids (oil, gas, water) saturation (Mohaghegh et al 1996). However, it is difficult to predict rock properties due to the form and spatial distribution of these heterogeneities, also the applicability of traditional analytical techniques such as multivariate regression are limited in this context.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is difficult to predict rock properties due to the form and spatial distribution of these heterogeneities, also the applicability of traditional analytical techniques such as multivariate regression are limited in this context. Several authors such as Mohaghegh et al (1996) and Handhel (2009) in their related researches buttress these complexities for predicting in heterogeneity reservoir in oil and natural gas field studies.…”

Section: Introductionmentioning

confidence: 99%

Generalized regression and feed-forward back propagation neural networks in modelling porosity from geophysical well logs

Konaté

Pan

Khan

et al. 2014

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

Geophysical formation evaluation plays a fundamental role in hydrocarbon exploration and production processes. It is a process which describes different reservoir parameters using well field data. Porosity is one of the parameters that determines the amount of oil present in a rock formation and research in this area is mainly carried out by engineers and geoscientists in the petroleum industry. Accurate prediction of porosity is a difficult problem. This is mostly due to the failure in the understanding of spatial porosity parameter distribution. Artificial neural networks have proved to be a powerful tool for mapping complicated and non-linear relationships in petroleum studies. In this study, we analyze and compare generalized regression neural network (GRNN) and feedforward back propagation neural network (FFBP) in modeling porosity in Zhenjing oilfield data. This study is calibrated on four wells of Zhenjing oilfield data. One well was used to find an empirical relationship between the well logs and porosity, while the other three wells were used to test the model's predictive ability in the field, respectively. The findings proved that the GRN network can make more accurate and credible porosity parameter estimation than the commonly used FFBP network. Artificial intelligence can be exploited as a powerful instrument for predicting reservoir properties in geophysical formation evaluation and reservoir engineering in petroleum industry.

show abstract

Petroleum reservoir characterization with the aid of artificial neural networks

Cited by 143 publications

References 3 publications

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm

Generalized regression and feed-forward back propagation neural networks in modelling porosity from geophysical well logs

Contact Info

Product

Resources

About