Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud Oil Field, Algeria

Ouahed, Abdelkader Kouider El; Tiab, Djebbar; Mazouzi, A.

doi:10.1016/j.petrol.2005.05.003

Cited by 55 publications

(13 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Type-2 fuzzy set logic is a generalization of conventional fuzzy logic (type-1) in the sense that uncertainty is not only limited to the linguistic variables but also is present in the definition of the membership functions [47]. In Type-2, the membership grade is itself a fuzzy set in the range [0,1]. The use of Type-2 fuzzy sets is now quite widespread [19,48].…”

Section: Fuzzification Of the Individual Factorsmentioning

confidence: 99%

See 1 more Smart Citation

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: Fuzzification Of the Individual Factorsmentioning

confidence: 99%

“…Naturally fractured reservoirs represent a significant percentage of oil and gas reservoirs throughout the world [1]. Hydraulic fracturing is a crucial technology for economically developing this type of reservoir.…”

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

“…The connection weights are analyzed after training. These weights relate to the average contributions of each input log to the network by following equation [27]:…”

Section: Neural Network Modelingmentioning

confidence: 99%

An artificial neural network for prediction of gas holdup in bubble columns with oily solutions

Amiri

Mehrnia

Barzegari

et al. 2011

Neural Comput & Applic

View full text Add to dashboard Cite

Gas holdup in a bubble column reactor filled with oil-based liquids was estimated by an artificial neural network (ANN). The ANN was trained using experimental data from the literature with various sparger pore diameters and a bubbly flow regime. The trained ANN was able to predict that the gas holdup of data did not seen during the training period over the studied range of physical properties, operating conditions, and sparger pore diameter with average normalized square error\0.05. Comparisons of the neural network predictions to correlations obtained from experimental data show that the neural network was properly designed and could powerfully estimate gas holdup in bubble column with oily solutions. KeywordsArtificial neural network (ANN) Á Bubble column reactor Á Liquid properties Á Oil-based liquids Á Total gas holdup List of symbols Ar Archimedes number d C Column diameter (m) d p Mean pore diameter (m) d S Sparger diameter (m) Eo Eotvos number Fr Froude number H L Liquid heights before gas injection (m) H D Liquid heights after gas injection (m) n 1

show abstract

“…Recently, artificial intelligence techniques were utilized successfully for prediction of different reservoir parameters (Ahmadi 2011;Wang et al 2013;Anifowose et al 2017). El Ouahed et al (2005 developed a 2-D fracture network map and fracture intensity map in Hassi Messaoud oil field by using fuzzy logic and artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Estimation of fracture aperture from petrophysical logs using teaching–learning-based optimization algorithm into a fuzzy inference system

Ghoochaninejad

Asef

Moallemi

2017

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

Aperture, which refers to the opening size of a fracture, is a critical parameter controlling rock mass permeability. Moreover, distribution of permeability within the reservoir is commonly affected by natural fracture occurrences. In a water-based mud environment, boreholeimaging tools are able to identify both location and aperture size of the intersected fractures, whereas in oil-based environment, due to invasion of resistive mud into the fractures, this technique is impractical. Recently, some artificial intelligence techniques facilitated reliable estimations of reservoir parameters. In this paper, a teachinglearning-based optimization algorithm (TLBO) trained an initial fuzzy inference system to estimate hydraulic aperture of detected fractures using well logs responses. Comparing the results with real measurements revealed that the model can provide reliable estimations in both conductive and resistive mud environments, wherever the aperture size is unknown. TLBO, besides of its easier application, outperformed earlier optimization algorithms, which were used to evaluate the method effectiveness.

show abstract

Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud Oil Field, Algeria

Cited by 55 publications

References 8 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

An artificial neural network for prediction of gas holdup in bubble columns with oily solutions

Estimation of fracture aperture from petrophysical logs using teaching–learning-based optimization algorithm into a fuzzy inference system

Contact Info

Product

Resources

About