Selection of Hydraulic Fracturing Candidates in Iranian Carbonate Oil Fields: A Local Computerized Screening of Zone and Well Data (original) (raw)
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… Journal of Computer …, 2012
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.
International Petroleum Technology Conference, 2013
Hydraulic Fracturing (HF) is a mature technique to rehabilitate the productivity of a hydrocarbon formation, but Iranian oil companies are taking the primary steps to practice it in their oil fields. A couple of operations have been practiced but the unproductive results emphasized on the importance of Candidate Selection method. There is not a standard procedure or computerized tool to select primary candidates from Iranian carbonate oil fields. This paper presents the development of a locally written interface to automatically select specific zones for special operations like HF. The program is written in MATLAB in such a way to anticipate the missing data by Neural Network and Fuzzy Logic technique and then integrate large amount of data from different disciplines. 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 easy to see the quality of each criteria. This technique can be applied for unlimited number of zones and wells. Introduction
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.
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
A Review on Conventional Candidate-well Selection for Hydraulic Fracturing in Oil and Gas Wells
2012
Hydraulic Fracturing (HF) which is an ever-increasing focus area for upstream industry is the pumping of fluids at high rates and pressures in order to break the rock, and it is using to accelerate hydrocarbon production and improving ultimate recovery in many reservoirs. It is clearly indicated in HF experience's literature, to be successful conducted, it is directly depending on rigorous candidate-well selection. The techniques applied in HF candidate-well selection could be divided into two methods; conventional and advanced approaches. Being familiar with the conventional methods in candidate-well selection that mainly deals with engineering, geological, etc aspects in decision making process, is of particular importance in order to increase the performance of the advanced techniques that mainly utilized artificial intelligence methods. This paper is a review of the conventional candidate-well selection for hydraulic fracturing in oil and gas wells.
Novel Applications Of Artificial Intelligence Neural Network In Hydraulic Fracturing
2020
Increasing productivity is a critical target for petroleum industry especially upon increased demand on petroleum products. The primary goal of a hydraulic fracturing treatment is to create a highly conductive flow path to the wellbore that economically increases well production, so Hydraulic frac is one of the major methods used to increase productivity if not the most efficient one. Field containing many wells makes it difficult to choose the most efficient one suitable for high productive frac .There are different screening criteria used, but still there are not sharp efficient, so I try in this research using artificial intelligence neutral networks to create a platform model for selecting the best well candidate for maximum overall productivity of an oil field, study the different affecting parameters on reservoir stimulation and predict the performance and future optimum designs . Artificial intelligence neural network is an information processing system simulating the natural...
Neural Computing and Applications, 2015
The problem of selecting a target formation(s) in a reservoir among a vast number of zones/sublayers within huge number of hydrocarbon producing wells for hydraulic fracturing (HF) by using interval type-2 fuzzy logic system (IT2-FLS) to maximize their net present value is studied in this paper. Classical fuzzy system which is called type-1 fuzzy logic system is not capable of accurately capturing the linguistic and numerical uncertainties in the terms used and the inconsistency of the expert's decision-making. IT2-FLS is very useful in circumstances where it is difficult to determine an exact membership function for a fuzzy set; hence it is very effective for dealing with uncertainties. In highlighting this need, the question has been answered why IT2-FLS should be used in this study. The procedure of applying this study in the area of HF candidate-well selection is illustrated through a case study in an oil reservoir.
2018
Hydraulic fracturing creates a high conductivity channel within a large area of formation and bypasses any damage that may exist in the near wellbore region. Moreover, it has been one of the major well stimulation techniques to increase well production. Accurate knowledge of parameters affecting fracture initiation pressure provides essential information to assess the identification of fracture initiation zones and hydraulic fracture strategies as well as completion design requirements. In order to study the feasibility of implementing this method, Sirii-A reservoir is selected and an extensive literature survey was carried out. Geomechanical model factors are calculated by poroelastic methods and normal stress regime (σν > σH > σh) is diagnosed for the reservoir rock. Based on the crucial factors such as in situ stress, porosity, water saturation and uniaxial compressive strength, best layers for hydraulic fracturing operation are selected and their fracture pressures are est...