Practical application of fuzzy logic and neural networks to fractured reservoir characterization

Ouenes, Ahmed

doi:10.1016/s0098-3004(00)00031-5

Cited by 133 publications

(55 citation statements)

References 11 publications

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“…Each of these approaches carries with it a set of advantages and limitations. Hence, it would seem natural that some attempts have been made to integrate them in order to obtain the best of all approaches [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Introductionmentioning

confidence: 99%

“…However, this increases the complexity of the process and may raise the number of parameters beyond the capacity of many conventional methods. Therefore, a way to assess all of the known criteria by combining methods would be very useful [25,26].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…It is known that with training an expert system from known fused input data, any complex type of nonlinear problems can be handled in the earth sciences (Ouenes, 2000). Recently, the neural network technique has gained the most attention as an accurate, fast method yielding results somewhat superior to the conventional methods due mainly to the independence from any prior knowledge about the nature of relationships between the input and output variables (Nikravesh and Aminzadeh, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the neural network technique has gained the most attention as an accurate, fast method yielding results somewhat superior to the conventional methods due mainly to the independence from any prior knowledge about the nature of relationships between the input and output variables (Nikravesh and Aminzadeh, 2001). In the last two decades, neural networks have been widely used in solving many different problems of upstream oil industry such as estimation of fundamental reservoir properties (Aminzadeh et al 1999;Lim, 2005;Liu and Liu, 1998;Ouenes, 2000;Verma et al 2012;Walls et al 2000;Wiener et al 1991), prediction of petrophysical properties from well log data (Fung et al 1997;Quirein et al 2000), prediction of complex lithologies (Benaouda et al 1999;Bueno et al 2006;Rogers et al 1992;Wang and Zhang, 2008), generating synthetic well logs (Rolon et al 2009), oil field production forecast (Chen and Lang, 2003;Liu et al 2008), predicting temperature profiles in producing oil wells (Farshad et al 2000) as well as ranking reservoirs in order of exploitation priority (Li et al 2008). …”

Section: Introductionmentioning

confidence: 99%

The application of multilayer perceptron neural network in volume of clay estimation: Case study of Shurijeh gas reservoir, Northeastern Iran

Jozanikohan

Norouzi

Sahabi

et al. 2015

Journal of Natural Gas Science and Engineering

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Volume of clay is an important component in the assessment of shaly sand reservoirs, due to its significant impact on the production characteristics. The Shurijeh sandstone Formation of Lower Cretaceous age, with subordinate shales is one of the most challenging gas reservoirs to be properly characterized in the eastern Kopet-Dagh sedimentary Basin, Northeastern Iran. This paper describes the improvement achieved in estimating the volume of clay in the Shurijeh reservoir Formation, with an application to a gas producing well and another nonproducing well in a joint field between Iran and Turkmenistan. A clear comparison between estimates from several conventional petrophysical methods and actual laboratory measured data of 76 core samples showed very large estimation errors; therefore an attempt has been made to improve the estimation with developing a multilayer feedforward backpropagation neural network. Six types of well logs were selected, through a sensitivity analysis, as the most relevant input data to the volume of clay (network output). Data were then standardized and randomly divided into three sets of 70% for training, 15% for validation and 15% for testing. Three different training algorithms of genetics, particle swarm optimization, and Levenberg-Marquardt were tested on a network with certain topology, and the latter was within data set from the Shurijeh Formation.

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“…A fuzzy-logic approach and neural networks have been applied to a variety of hydrological problems, such as: rainfall forecasting (Bodri & Cermak, 2000;Luk et al, 2001); streamflow forecasting (Chang & Chen, 2001;Kim & Barros, 2001;Campolo et al, 2003;Hu et al, 2005); groundwater level prediction (Giustolisi & Simeone, 2006); modelling the infiltration process (Sy, 2006); reservoir operations (Ouenes, 2000;Hasebe & Nagayama, 2002;Chang et al, 2005); rainfall-runoff modelling (Wilby et al, 2003;Giustolisi & Laucelli, 2005); parameter estimation of hydrological models (Rowinski et al, 2005); uncertainty analysis of hydrological model parameters (Ozelkan & Duckstein, 2004); and modelling of time series (Sisman-Yilmanz, et al, 2004;Nayak et al, 2004). Jang (1993) developed the adaptive neuro-fuzzy inference system (ANFIS).…”

Section: Introductionmentioning

confidence: 99%

Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique

Bae¹,

Jeong²,

Kim

2007

Hydrological Sciences Journal

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The purpose of this study is to evaluate the applicability of monthly weather forecasting information to the improvement of monthly dam inflow forecasts. The ANFIS (Adaptive Neuro-Fuzzy Inference System) is used to predict the optimal dam inflow, since it has the advantage of tuning the fuzzy inference system with a learning algorithm. A subtractive clustering algorithm is adopted to enhance the performance of the ANFIS model, which has a disadvantage in that the number of control rules increases rapidly as the number of fuzzy variables increases. To incorporate weather forecasting information into the ANFIS model, this study proposes a method for converting qualitative information into quantitative data. The ANFIS model for monthly dam inflow forecasts was tested in cases with and without weather forecasting information. It can be seen that the model performances obtained with the use of both past observed data and future weather forecasting information are much better than those using past observed data only.Key words neuro-fuzzy system; weather forecasting information; subtractive clustering; dam inflow Prévisions mensuelles de l'afflux dans un barrage en utilisant des informations de prévision météorologique et une technique neuro-floueRésumé Le but de cette étude est d'évaluer l'applicabilité de prévisions météorologiques mensuelles pour améliorer des prévisions mensuelles d'afflux dans un barrage. On utilise la méthode neuro-floue ANFIS (système adaptatif d'inférence neuro-floue) pour prévoir l'afflux optimal dans le barrage, parce qu'elle a l'avantage de régler le système d'inférence floue avec un algorithme d'apprentissage. Un algorithme soustractif de classification est adopté pour rendre plus performant le modèle ANFIS, dont un désavantage est que le nombre de règles de contrôle augmente rapidement avec le nombre de variables floues. Pour intégrer les informations de prévision météorologique dans le modèle ANFIS, cette étude propose une méthode de conversion des informations qualitatives en données quantitatives. Le modèle ANFIS de prévision mensuelle de l'afflux dans le barrage a été testé avec et sans les informations de prévision météorologique. Il apparaît que les performances du modèle obtenues en combinant des données du passé et des prévisions météorologiques sont bien meilleures que celles qui sont obtenues avec les seules données du passé.Mots clefs système neuro-flou; informations de prévision météorologique; classification soustractive; afflux dans un barrage

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Practical application of fuzzy logic and neural networks to fractured reservoir characterization

Cited by 133 publications

References 11 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

The application of multilayer perceptron neural network in volume of clay estimation: Case study of Shurijeh gas reservoir, Northeastern Iran

Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique

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