fracturing in oil and gas wells: A critical review (original) (raw)
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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.
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.
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
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.
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.
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
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...
An analitical study of hydraulic fracturing optimization for tight shale formation
21th International Petroleum and Natural Gas Congress and Exhibition of Turkey, 2023
Hydraulic fracturing optimization is a critical aspect of improving the recovery of unconventional shale formation. This paper discusses the use of different types of proppants, rate optimization, and proppant amount optimization to improve hydraulic fracturing techniques. The paper begins with a discussion of proppant selection, which is a critical aspect of hydraulic fracturing. The authors highlight the importance of proppant endurance in holding the fracture opening and provide a range of proppants suitable for different confining pressures. Tables and charts are included to illustrate the permeability values of various proppants under different closure stress values. This section also emphasizes the significance of proppant shape in creating a more conductive path in the fracture. The next section of the paper discusses the methodology used in the study, including the Fracpro software simulation parameters. The authors then delve into the optimization of proppant specific gravity and the results of their experiments with five different types of proppants. The paper highlights the impact of proppant specific gravity on fracture width and dimensionless conductivity (FCD). The authors also focus on the optimization of pumping rate, which is an essential parameter of hydraulic fracturing operations. The paper includes simulation studies conducted to determine the effects of pumping rate on fracture parameters such as propped length and propped height. The authors highlight the relationship between rate and FCD and how it is affected by permeability values of the proppant. Finally, the paper discusses proppant amount optimization, which is a critical point of hydraulic fracturing optimization. The authors provide an overview of the results of the experiments conducted to determine the optimal amount of proppant required for different hydraulic fracturing operations. Overall, this paper provides valuable insights for researchers and engineers working to improve hydraulic fracturing techniques for tight shales formation. The authors use a combination of theory, experiments, and charts to provide a comprehensive overview of the various aspects of hydraulic fracturing optimization.
… 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.