Applications of type-2 fuzzy logic system: handling the uncertainty associated with candidate-well selection for hydraulic fracturing (original) (raw)
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Hydraulic Fracturing Candidate-Well Selection by Interval Type-2 Fuzzy Set and System
International Petroleum Technology Conference, 2013
Selecting a target formation(s) among a vast numbers of zones/sub-layers within huge numbers of hydrocarbon producing wells in a reservoir, is considered a difficult task, particularly if the selection goes through a group of parameters having different attributes and features; such as geological aspect, reservoir and fluid characteristics, etc. The trend of candidate-well selection (CWS) process for Hydraulic Fracturing (HF) had recognized to be complex, nonlinear, un-equilibrium, and adherent with uncertainty. Interval Type-2 Fuzzy Logic and Systems (IT2-FLSs) are very useful in circumstances where it is difficult to determine an exact membership function (MF) for a Fuzzy Set (FS); hence they are very effective for dealing with uncertainties. Classical FLS which called T1-FLS is not capable of fully capturing the linguistic and numerical uncertainties in the terms used and the inconsistency of the expert's decision-making. Therefore, the need arises to use a method that could handle uncertainties. The procedure of applying this novel study in the area of HF CWS, will have illustrated through a case study in a carbonate reservoir. The utilization of a modern and right problem-solving tool such as T2-FSS should be considered a great concern to the petroleum industry. Although sizeable clarity has been achieved in this area, no conceptualization such as dealing with uncertainty, has yet answered by the previous studies. New requirements force the previous methods to advance and novel techniques expected to meet the requirements and remove the existing weakness. In highlighting this need, the question has been answered about why IT2-FLSs should be used in this study. Also, its advantages over T1-FLS will be illustrated. This paper critically assesses the importance of the proposed methodology to develop a reliable model of HF CWS. This investigation is the first research which applied such a cutting-edge approach and tries to fill the gap between recent developments in uncertainty management through utilization of IT2-FLSs in HF candidate-well selection. TX 75083-3836, U.S.A., fax +1-972-952-9435
During the last two decades, Fuzzy Logic (FL) Systems have been increasingly applied to the research area of petroleum engineering. Hydraulic Fracturing (HF) as an important discipline in this area and stimulation of oil and gas wells is an important tool for natural gas and oil exploitation, which its success relies on proper selection of target well and target formation. To date, however, no paper has attempted to summarize and present a critique of the existing FL literature. This paper, therefore, aims to review the FL literature that has been conducted in candidate-well selection for HF. Selecting a target formation(s) among a vast numbers of zones/sub-layers within huge numbers of hydrocarbon-producing wells in a reservoir is considered a difficult task, particularly if the selection goes through a group of parameters having different domains, attributes and features. In fact, this process had recognized to be complex, nonlinear, and adherent with uncertainty. It is proved that methods such as FL could reduce the uncertainty thus permit superior selection of candidates well for HF treatment. The comprehensive review provided in this paper offers new directions for FL and its application in HF candidate-well selection.
American Journal of Operations Research, 2014
Hydraulic fracturing is widely used to increase oil well production and to reduce formation damage. Reservoir studies and engineering analyses are carried out to select the wells for this kind of operation. As the reservoir parameters have some diffuse characteristics, Fuzzy Inference Systems (FIS) have been tested for these selection processes in the last few years. This paper compares the performance of a neuro fuzzy system and a genetic fuzzy system used for selecting wells for hydraulic fracturing, with knowledge acquired from an operational data base to set the SIF membership functions. The training data and the validation data used were the same for both systems. We concluded that, despite the genetic fuzzy system being a newer process, it obtained better results than the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy system can work with constraints, the membership functions setting kept the consistency of variable linguistic values.
Journal of Petroleum Exploration and Production Technology, 2018
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.
Journal of Systems Science and Systems Engineering, 2017
Hydrocarbon prospective zone is a certain layer in a reservoir which is estimated producing oil. The geologists use the qualitative analysis method to find the prospect layers. The research used five variables modeled by three fuzzy membership functions and eight rules of fuzzy logic. The rules cause insensitiveness in the working system. This study therefore was conducted by modeling each of input variables into different models using 36 rules. It aims to determine the existence of hydrocarbon prospective zone through a qualitative analysis in a reservoir using fuzzy inference system with Mamdani method. The data were taken from well log data in reservoir “X”. There were some steps in doing this study, including fuzzification, inference system, and defuzzification. The result showed 99 prospect layers from 3000 layers in reservoir “X” with 97.7% of accuracy.
Practical application of fuzzy logic and neural networks to fractured reservoir characterization
Until recently most fractured-reservoir modeling tools were limited to simple discrete statistical models. A new approach in fractured-reservoir characterization which uses arti®cial intelligence tools is described in this paper. The methodology is based on the assumption that there is a complex relationship between a large number of potential geologic drivers and fractures. Structure, bed thickness and lithology are a few of the drivers that played a role when fractures where created.
Applications of fuzzy experts systems in integrated oil exploration
Computers & Electrical Engineering, 1994
Expert systems, though not widely used in the oil industry, have been the subject of a large volume of research and development activities in the industry and academia. Two reasons for limited practical usage of expert systems in oil exploration are: (1) exploration in general is highly multidisciplinary; and (2) the rules governing the exploration process are, for the most part, subjective. The combination of these two factors has made development of expert systems for solving practical exploration problems difficult. Recent advances in some areas of expert systems, coupled with the availability of cost effective and fast workstations, offer opportunities to overcome the two major obstacles. Specifically, using fuzzy logic networks in expert systems makes integration of different knowledge sources, implementation of inexact and qualitative rules (information), and self-learning more practical.
2013
Iranian oil companies are developing the technique of Hydraulic Fracturing (HF) operation to enhance the hydrocarbon recovery of deep carbonate formations. However, there is not a computerized tool or well defined framework for Iranian carbonate oil fields to select candidates. The ineffective HF experiences in the past emphasized that candidate selection is the frontline of a victorious HF operation. This paper presents the development of a local programme to automatically select specific zones for special purposes like HF. The program is written in MATLAB in such a way to integrate large amount of data from different disciplines. In addition, the missing data are compensated with Neural Network and Fuzzy Logic techniques. In the end data are mechanically screened based on the user selected parameters, cut-offs and weight factors. Results of screening within the limitations are prioritized in stacked bars to make decision easier. This tool is applied for a purpose of candidate selection for HF in M oil field located in south of Iran. This field has 585 zones which each zone has more than 30 parameters form different disciplines. The result of this programming is printed schematically and it is conclusive to our clients.
Changes such as ruptured tubing and/or casing, plugged perforations or tubing etc. might occur in the reservoir, or near wellbore zone. Therefore, it becomes necessary to service the well so as to maintain and improve oil and gas production or injection performance. Candidate well selection is the process of identifying and selecting wells for remedial operations which have the capacity for higher production and better economic return. Not all wells are considered suitable for workover operations. The conventional approach of candidate well selection makes use of engineering and geological analysis which requires a large amount of manual work and this process is very time consuming. Fuzzy Logic as one of the emerging techniques of artificial intelligence has as its main advantage the capacity to embed human knowledge into useful algorithms. This technique performs well when no mathematical model is obtainable for a problem as is the case with workover candidate wells selection. This research project presents a Mamdani fuzzy logic evaluator where rules for acidizing candidate-well selection were derived from existing literatures. Ten wells were screened against six criteria. From the results it was observed that three wells were excellent candidates. Another three wells were good candidates; two wells were possible candidates while two wells were not candidates for stimulation by acidizing operation. Error analysis was carried out to ascertain the accuracy of the evaluator, R-squared value of 0.98 was obtained indicating a good performance by the evaluator. The results of the fuzzy logic evaluator were reasonable based on the fact that good wells are good candidates for stimulation. Based on the results obtained and the ability of fuzzy logic models to handle high uncertainty, this AI technique is highly recommended for use in inference modelling where high imprecision and uncertainty is involved.