Remote Sensing, Fuzzy Logic and GIS in Petroleum Exploration

Taheri, Reza; Tait, Alan Melville

doi:10.2118/101040-ms

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Fuzzy logic helps to guarantee precision and quality and to avoid inconsistency and uncertainty (Bilgen, 2010). Fuzzy logic applications in E&P operations have been reported in different areas such as engineering design and control (Nikravesh et al 1997;Sengul and Bekkousha 2002;Mohaghegh et al 2005;Widarsono et al 2005;Cao et al 2006;Taheri 2008;Weiss et al 2001;Zarei et.al. 2008;Murillo et al 2009) and also in operations of production facilities, systems and optimal well operations (Rivera and Farabee 1994;Dumans 1995;Xiong et al 2001;Alimonti and Falcone 2004;Garrouch and Lababidi 2005)…”

Section: Application Of Fuzzy Logic In Eandpmentioning

confidence: 99%

A review of fuzzy logic applications in petroleum exploration, production and distribution operations

Okwu

Nwachukwu

2018

J Petrol Explor Prod Technol

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Petroleum exploration and production business thrives with in-depth knowledge and understanding of the subsurface. Technological advancement has helped in furnishing the industry with much information about the petroleum reservoir; however, a lot of uncertainties still exist due to the nature of the subsurface. The industry has strived to address this problem in diverse ways; regrettably, the classical methods relied upon have failed to provide a proper guide to management decision in exploiting these reservoirs. In recent times, artificial intelligence techniques, particularly Fuzzy Logic (FL), have been identified as a potential tool to deal with the uncertainties encountered in most exploration and production (E&P) operations. This research provides a review of FL applications in E&P operations under non-deterministic input parameters, possible challenges and solution procedures using FL sensitivity analysis and rule viewers. The focus is on reservoir characterization for reservoir evaluation, drilling/completion operations and stimulation treatment. The study also examines the extent FL could be applied to extract useful information from the large volume of historical oil and gas data already on the shelf and the future gaps to fill. A case study was presented which considered cost optimization in Liquefied Petroleum Gas (LPG) distribution operations using fuzzy logic.

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Section: Application Of Fuzzy Logic In Eandpmentioning

confidence: 99%

A review of fuzzy logic applications in petroleum exploration, production and distribution operations

Okwu

Nwachukwu

2018

J Petrol Explor Prod Technol

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“…In the latter, GIS are used as platforms, from which spatial analysis functions are used, and with which mathematical models are integrated to complete reservoir evaluation. In terms of mathematical models, fuzzy mathematics (Schrader, Balch, & Ruan, ; Taheri, ; Zoveidavianpoor, Samsuri, & Shadizadeh, ), gray clustering (Denney, ), artificial neural networks (Ahmadi, Saemi, & Asghari, ; Elshafei & Hamada, ; Wang, Wang, & Chen, ), gray multivariate correlation analysis (Naseri, Khishvand, & Sheikhloo, ; Shi, ), rough sets (Wu, Li, Dai, & Feng, ), support vector machines (Ahmadi, ), and geostatistical methods (Tamaki, Suzuki, Fujii, & Sato, ) are studied and/or integrated with GIS.…”

Section: Introductionmentioning

confidence: 99%

A geospatial case‐based reasoning model for oil–gas reservoir evaluation

Chen

Wang

Jun-wen³

2018

Transactions in GIS

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Delineating the distribution of oil and natural gas resources is a prerequisite of exploitation. The delineation methods usually include conventional techniques of reservoir evaluation and mathematical models. The conventional reservoir evaluation results generally depend on the experts' knowledge and experience in the field. The mathematical methods mostly require accurate models to be proposed. Considering spatial relationships and characteristics of geological reservoir problems, including nonlinearity, complexity and uncertainty, a novel model called geospatial case-based reasoning for oil-gas reservoir evaluation was proposed in this article. The key components of the new model, including: (1) the joint representation of spatial relationship and attribute features; (2) the model of spatial relationship and attribute similarity joint reasoning; and (3) the methods of establishing weights for the spatial relationship and attribute features, are completely constructed. A case study of the proposed model for gas reservoir evaluation was carried out. Compared with the backpropagation artificial neural network (BP-ANN) and the geological empirical evaluation (GEE) methods, the model presented in this article performs6.38% and 46.81% better than BP-ANN and GEE, respectively. Furthermore, its execution is simpler, more convenient, and importantly, its utilization hardly requires any professional knowledge of the field.

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“…An extensive list of such applications is presented in Table 1. In particular, many applications are focused on engineering-design and control problems (Sengul and Bekkousha 2002;Mohaghegh et al 2005;Widarsono et al 2005;Nikravesh et al 1997;Taheri 2008;Weiss et al 2001;Cao et al 2006;Zarei et al 2008;Wu et al 1997;Murillo et al 2009;Kanj and Roegiers 1999;Hajizadeh 2007b;Garrouch and Al-Ruhaimani 2003) relating to the operation of production systems and facilities. Neuroth et al (2000) applied fuzzy logic to the control of pump stations for oil-and gas-transport systems, and their method resulted in a reduction of the maintenance and operational costs.…”

Section: Introductionmentioning

confidence: 99%

Production-System Optimization of Gas Fields Using Hybrid Fuzzy/Genetic Approach

Park¹,

Lim²,

Roh³

et al. 2010

SPE Journal

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The design of production systems of gas fields is a difficult task because of the nonlinear nature of the optimization problem and the complex interactions between each operational parameter. Conventional methods, which are usually stated in precise mathematical forms, cannot include the uncertainties associated with vague or imprecise information in the objective and constraint functions.This paper proposes a fuzzy nonlinear programming approach to accommodate these uncertainties and applies it to a variety of optimization processes. Specifically, the fuzzy -formulation is combined with a hybrid coevolutionary genetic algorithm for the optimal design of gas-production systems. Both the multiple conflicting objective and constraint functions for production systems of gas fields are formulated in a feasible fuzzy domain. Then, the genetic algorithm is used as a primary optimization scheme for solving the optimum gas-production rates of each well and the pipeline segment diameters to minimize the investment cost with a given set of constraints in order to enhance the ultimate recovery.The synthetic-optimization method can find a global compromise solution and offer a new alternative with significant improvement over the existing conventional techniques. The reliability of the proposed approach is validated by a synthetic practical example yielding more-improved results. This method constitutes an offering of a powerful tool for cost savings in the planning and optimization of gas-production operations.

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Remote Sensing, Fuzzy Logic and GIS in Petroleum Exploration

Abstract: TX 75083-3836, U.S.A., fax 01-972-952-9435.

Cited by 6 publications

References 4 publications

A review of fuzzy logic applications in petroleum exploration, production and distribution operations

A review of fuzzy logic applications in petroleum exploration, production and distribution operations

A geospatial case‐based reasoning model for oil–gas reservoir evaluation

Production-System Optimization of Gas Fields Using Hybrid Fuzzy/Genetic Approach

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