Development of an Artificial Neural Network for Dual Lateral Horizontal Wells in Gas Reservoirs PDF Download
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Author: Nilsu Kistak
Publisher:
ISBN:
Category :
Languages : en
Pages : 114
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Author: Nilsu Kistak
Publisher:
ISBN:
Category :
Languages : en
Pages : 114
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Author: Jacob Cox
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Category :
Languages : en
Pages :
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The goal of this study was to create a tool with the use of an artificial neural network (ANN) that could quickly predict the inverse solution to pressure transient (PT) data created for multiple multi-lateral wells completed within a tight-gas dual-porosity reservoir. This inverse tool would be able to predict the user's reservoir parameters nearly instantaneously with known inputs of PT data and wellbore design. This tool will take ideas from current well test analysis to aid in the training of the neural network. However, once the network has been trained, it will be able to predict multiple key reservoir properties and the time consuming process of conventional well test analysis will no longer be an issue.
Author: Hussain Gaw
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Languages : en
Pages :
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In order to meet the increasing demand for natural gas, it has become important to increase production. Drilling horizontal wells in naturally fractured gas reservoirs can greatly help in achieving the desired high gas production. Reservoir simulation is used to history match production profiles in order to predict important reservoir characteristics. Nevertheless, the use of commercial simulators is time consuming. Therefore, the search for alternatively fast means to predict reservoir properties promoted the use of artificial neural networks. These networks have grown in popularity because of their ability to solve non-linear relationship problems, generate accurate analysis and predict results from large number of historical data. Artificial neural network (ANN) is a mathematical model, which tries to mimic the structure and functionality of a human biological network in acquiring, storing and using experimental knowledge. ANN predicts target outputs when given a set of input and it can be trained until optimum results are reached. The main objective of this study is to develop artificial neural networks that will perform pressure and rate transient analysis for dry, wet and condensate gas reservoirs with fixed composition. The network is given three main variables: production profiles, well parameters and reservoir characteristics, where each variable can be predicted in the presence of the other two. Three separate artificial neural networks were trained for each of the three models. These artificial neural network showed good results and were able to predict the desired outputs with an average error less than 10%.
Author: Sinan Oz
Publisher:
ISBN:
Category :
Languages : en
Pages : 117
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Author: Chris Raymond Affane Nguema
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Category :
Languages : en
Pages :
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The increasing demand in energy has strengthen the dependence on fossil fuels. On the other hand, the conventional hydrocarbon reservoirs are depleting rather quickly which prompted an important study of the hydrocarbon reservoirs from unconventional reservoirs. Over the last two decades, the improvement in technology and recovery methods has allowed the industry to extract hydrocarbon from unconventional reservoirs. There are been important advancements in drilling and reservoir engineering technologies.In order to overcome some of the costs associated with the exploitation of those reservoirs, an extensive use of techniques such as directional drilling to has been largely recommended and has proven to be more efficient. Directional drilling allows to control the direction of the wellbore to increase the contact with the target or pay zone location among other significant benefits.Reservoir simulation refers to constructing computer models to gain a better understanding of reservoirs. It is mostly used to predict the flow of fluids or to match the properties of the reservoir. However, it has shown to have be limited when not enough information about the reservoir is available.Artificial neural network (ANN) is a technique used in many fields that has been able to compensate for some of the limitations associated with other approaches such as reservoir simulation. It relies on observed data to build highly non-linear and strong links among them that make it possible to obtain a more accurate prediction of the missing information.The main goal of this study is to develop an ANN tool for a single phase unconventional gas reservoir that can predict reservoir properties such as porosity, permeability and compressibility. The tool applicability has been extended for a large range of data. It provides predictions from two network structures, cascade forward backpropagation and radial basis function with an option to compare them. Each of the ANN model, therefore, differs by the type of networks used, the porosity system (single or dual), the well inclination (0 to 90), and the transient data available (pressure or production rate). The performance of each network was evaluated using the average percent error, the mean bias error (MBE), and the root mean square error (RMSE).
Author: Mohammed Alquisom
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Languages : en
Pages :
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The oil and gas industry is continuously trying to increase hydrocarbons recovery in order to meet the high demand for energy in the world. Increasing the production rate of hydrocarbon compromises the lifespan of the reservoir. Throughout last decays, a number of processes have been developed in the oil and gas industry to increase the hydrocarbon recovery while minimizing their effect on the life of the reservoir. One of these techniques is the horizontal well drilling. This drilling method allows higher recovery of hydrocarbons by increasing the contract area between the casing and the oil zone. However, high production rate from the horizontal well will result in phenomenon called cresting. The time at which it occurs is called breakthrough time. The goal for any production engineer is to delay breakthrough time as much as possible. The delay of this time will result in increasing the lifetime of the reservoir by maintaining the natural driving forces represented by water drive and gas cap in the reservoir.In this study artificial neural network is utilized to construct a reliable tool to predict the production profiles namely: oil rate, gas rate, water rate, cumulative oil, cumulative gas, cumulative water, gas oil ratio, water oil ration and water cut, that lies within the reservoir and design properties for this study. A synthetic three-phase reservoir with a gas cap and bottom water drive is constructed using a commercial reservoir simulator to simulate and validate. After that, 600 different scenarios were generated using a range of reservoir properties along with different depth at which horizontal well will be placed. These different scenarios were used to train the ANN in order to make it predict the production profiles mentioned above within an error range of 5-15%. A graphical user interface (GUI) was developed to make this model user-friendly. A user will be asked to input the required reservoir properties and the design property in the form of numbers and then the user will be able to obtain production profiles along with gas oil ratio, water oil ratio and water cut profiles.
Author: Ihsan Burak Kulga
Publisher:
ISBN:
Category :
Languages : en
Pages : 121
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Author: Khaled Enab
Publisher:
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Category :
Languages : en
Pages :
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Increasing oil recovery and decreasing production costs in unconventional reservoirs is in great demand due to current low oil prices. The oil price drop affected unconventional oil production to a greater degree than conventional oil production because of its low production rates and high production costs. The objective of increasing oil recovery and decreasing production costs of the unconventional oil reservoirs leads to an introduction of new technology and techniques. Many studies have been conducted to identify new techniques with high capabilities of increasing oil production from unconventional resources. Unconventional oil reservoirs are the reservoirs that cannot feasibly produce oil using conventional production techniques. These reserves include tight oil, oil shale, and bitumen.The first objective of this work is to introduce a new production technique that is capable of increasing oil recovery, decreasing production costs for unconventional oil reservoirs, and decreases the greenhouse gas emissions. The second objective is to build an Artificial Neural Network (ANN) based toolbox to evaluate and optimize the implementation of the proposed technique. The proposed ANN toolbox provides the necessary knowledge to understand the performance of the CO2 injection technique in low permeability reservoirs when utilizing fishbone well designs. A better understanding of how the implementation of the fishbone well design and CO2-WAG injection affects the production from low permeability reservoirs, will help to increase oil recovery, decrease production costs, and decrease environmental impacts. A better understanding of how the implementation of the fishbone well design and CO2-WAG injection affects the production from low permeability reservoirs will help to increase oil recovery, decrease production costs, and decrease environmental impacts. The proposed production technique combines two successfully applied techniques in the unconventional reservoir production. The first technique is the Water Alternate Gas (WAG) injection technique implementation with carbon dioxide. The second technique is the fishbone multilateral well designs. The proposed techniques have successfully proven their capability of increasing oil production from unconventional reservoirs, but there are no previous studies that combined both techniques in the same project.Water alternate gas is a known term that describes an enhanced oil recovery process whereby water injection and gas injection are carried out alternately for periods of time to provide better sweep efficiency and reduce gas channeling from injector to producer. Carbon dioxide (CO2) is the most commonly used gas for this process because it improves hydrocarbon contact time and sweep efficiency while decreases the greenhouse gas emissions. The fishbone well design is a multi-lateral well technique which has been successfully used to improve production from low permeability reservoirs. The fishbone well design is a series of multilateral well segments that trunk off a main horizontal well. The appearance of the fishbone well closely resembling the ribs of a fish skeleton deviated from the main backbone. Fishbone well design increases the production by increasing the contact area with the reservoir. The utilization of the multi-lateral well technique increases the cumulative fluid recovery, decreases the environmental footprint and decreases the drilling and completion costs. An Artificial Neuron Network (ANN) is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN due to the adaptions it makes to provide the desired output. ANNs are considered nonlinear statistical data modeling tools where the complex relationships between inputs and outputs are modeled.ANN toolbox has been developed to evaluate and optimize the implementation of CO2-WAG injection in low permeability reservoirs using fishbone well design. The developed toolbox provides the needed information to develop a successful production plan for the reservoir under consideration. The designed toolbox has four primary functions. The first function is to predict the fluid flow rate profiles for the reservoir under consideration, with fishbone well and WAG injection implementation. The second function is to provide a reliable WAG injection design for a project with known reservoir properties while also creating a functional fishbone well after the field goes through a period of primary production. This will achieve the desired level of recovery for the project. The third function is to provide a reliable project design for the reservoir under consideration. The project design consists of a fishbone well design and a WAG injection design to achieve a desired level of recovery. The fourth function is to understand the reservoir properties using the production data. The toolbox can compare thousands of WAG designs and multilateral fishbone well design combinations in a more rapid manner when compared to commercial simulators.
Author: Khaled Enab
Publisher:
ISBN:
Category :
Languages : en
Pages : 84
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Author: Miltiadis D. Lytras
Publisher: MDPI
ISBN: 303651161X
Category : Technology & Engineering
Languages : en
Pages : 278
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Book Description
Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that.
