Application of Using Fuzzy Logic as an Artificial Intelligence Technique in the Screening Criteria of the EOR Technologies

Nageh, Mohamed; Ela, Mahmoud Abu El; Tayeb, El Sayed El; Sayyouh, Helmy

doi:10.2118/175883-ms

Cited by 15 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nashawi and Malallah (2015) used fuzzy logic for wireline well logs permeability prediction. Nageh (2015) developed a screening criterion for EOR technologies using artificial intelligence techniques. Thong and Kepic (2015) developed fuzzy clustering techniques for incorporating prior information into seismic impedance inversion.…”

Section: Review Fuzzy Logicmentioning

confidence: 99%

Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry

Vilela

Oluyemi

Petrovski

2020

IJADS

View full text Add to dashboard Cite

Value of information is a widely accepted methodology for evaluating the need to acquire new data in the oil and gas industry. In the conventional approach to estimating the value of information, the outcomes of a project assessment relate to the decision reached by following Boolean logic. However, human thinking is based on a more complex logic that includes the ability to process uncertainty. In value of information assessment, it is often desirable to make decisions based on multiple economic criteria which, if independently evaluated, may suggest opposite decisions. Artificial intelligence has been used successfully in several areas of knowledge, increasing and enhancing analytical capabilities. This paper aims at enriching the value of information methodology by integrating fuzzy logic into the decision-making process; this integration makes it possible to develop a human thinking assessment and coherently combine several economic criteria. To the authors' knowledge, this is the first use of a fuzzy inference system in the specified knowledge domain. The methodology is successfully applied to a case study of an oil and gas subsurface assessment where the results of the standard and fuzzy methodologies are compared, leading to a more robust and complete evaluation. Sensitivity analysis is undertaken for several membership functions used in the case study to assess the impact that shifting, narrowing and stretching the membership relationship has on the value of information. The results of the sensitivity study show that, depending on the shifting, the membership functions lead to different decisions; additional sensitivities to the type of membership functions are investigated, including the functions' parameters.

show abstract

Section: Review Fuzzy Logicmentioning

confidence: 99%

Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry

Vilela

Oluyemi

Petrovski

2020

IJADS

View full text Add to dashboard Cite

show abstract

“…Reference [4] [6]- [13] have mentioned parameters such as permeability, API o gravity and viscosity as suitable parameters for characterising EOR reservoirs. Few authors also included porosity and reservoir thickness as useful parameters ( [5] [14]- [19]). In all these EOR criteria, the authors have not investigated the effect of combinatorial quantities, such as mobility, momentum and transmissibility, in characterising EOR reservoirs and screening criteria.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of the Mobility Profile of Enhanced Oil Recovery Gases

Abunumah¹,

Ogunlude²,

Gobina³

2021

ACES

View full text Add to dashboard Cite

The mobility profiles of gases used in enhanced oil recovery (EOR) have been thoroughly investigated through the coupling operations of data mining of oilfield data and experimental data analyses. Mobility as an EOR objective function has not been previously applied to characterize potential reservoirs for EOR selection and application, even though it is a robust combinatorial function that benefits from two petrophysical variables, permeability and viscosity. The data mining approach identified mobility as a reliable objective function for reservoir characterisation. The data distribution and clustering results indicate that Gas EOR reservoirs have relatively higher mean mobility than Thermal, Microbial and Chemical EOR reservoirs. The experimental approach investigated EOR gases, CO 2 , CH 4 , N 2 , and Air. A modified Darcy Equation of State for gas flow through porous media was applied to evaluate which gas would competitively attain the oil displacement optimisation criterion for mobility ratio, M ≤ 1. Coupling the data mining with the experimental data results reveals that gas reservoirs can be further categorized by mobility. CH 4 (18.16 mD/cp) was observed to have the highest mobility followed by Air (14.60 mD/cp), N 2 (13.61 mD/cp), and CO 2 (12.96 mD/cp). The gas mobility order significantly corresponds with the mobility distribution of reservoirs that implemented gas EOR processes. It was concluded that CO 2 offers relatively lower mobility, therefore, it is the most competitive EOR gas to approach the mobility ratio criterion of unity or less.

show abstract

“…Subsequently, several works have been published to improve the quality and accuracy of the models. These models are based on fuzzy-logic (FL) and expert system approach [10,11] artificial neural network (ANN) [12] least square support vector machine (LSSVM), and very recently, the combination of both fuzzy-logic (FL) and neurofuzzy (NF) [13,14]. These works and others recently published in literature on screening techniques are summarised in [14].…”

Section: Introductionmentioning

confidence: 99%

Screening selection of enhanced oil recovery methods based on analytics of worldwide oilfield data with reference to offshore oil fields in Vietnam

Hien¹,

Long²,

Ngoc³

2021

PVJ

View full text Add to dashboard Cite

Selecting a proper enhanced oil recovery (EOR) method for a prospective reservoir is a key factor for successful application of EOR techniques. Reservoir engineers usually refer to screening guidelines to identify potential EOR processes for a given reservoir. However, these guidelines are often too general. In this study, we develop an advanced EOR screening technique based on the statistical analyses with boxplot in combination with some initial deep learning analyses to select the most suitable EOR method for a given mature oil field. At first, a database and the screening guidelines were established by compiling the information of 1,098 EOR projects from various publications in different languages, including Oil and Gas Journal (OGJ) biannual EOR surveys, SPE publications, DOE reports, and Chinese publications, etc. Boxplots were used to detect the special cases for each reservoir/fluid property and to present the graphical screening results. A case study was used to demonstrate that with a simple input of reservoir/fluid information, the proposed procedure could effectively give recommendations for EOR method selection. With the inputs (reservoir and fluid properties) from Vietnam offshore oil fields, the EOR methods recommended by this study are mostly chemical, including polymer and surfactant injection.

show abstract

Application of Using Fuzzy Logic as an Artificial Intelligence Technique in the Screening Criteria of the EOR Technologies

Cited by 15 publications

References 8 publications

Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry

Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry

Experimental Evaluation of the Mobility Profile of Enhanced Oil Recovery Gases

Screening selection of enhanced oil recovery methods based on analytics of worldwide oilfield data with reference to offshore oil fields in Vietnam

Contact Info

Product

Resources

About