Estimation of lost circulation amount occurs during under balanced drilling using drilling data and neural network

Lost circulation can cause an increase in time and cost of operation. Pipe sticking, formation damage and uncontrolled flow of oil and gas may be consequences of lost circulation. Dealing with this problem is a key factor to conduct a successful drilling operation. Estimation of lost circulation amo...

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Bibliographic Details
Main Authors: Pouria Behnoud far, Pantea Hosseini
Format: Article
Language:English
Published: Elsevier 2017-09-01
Series:Egyptian Journal of Petroleum
Subjects:
UBD
Online Access:http://www.sciencedirect.com/science/article/pii/S1110062116300575
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spelling doaj-392264704cb24f18ab328c6544e7663a2020-11-25T00:21:01ZengElsevierEgyptian Journal of Petroleum1110-06212017-09-0126362763410.1016/j.ejpe.2016.09.004Estimation of lost circulation amount occurs during under balanced drilling using drilling data and neural networkPouria Behnoud far0Pantea Hosseini1Drilling Engineering, Amirkabir University of Technology, IranSoftware Engineering, Islamic Azad University of Isfahan, IranLost circulation can cause an increase in time and cost of operation. Pipe sticking, formation damage and uncontrolled flow of oil and gas may be consequences of lost circulation. Dealing with this problem is a key factor to conduct a successful drilling operation. Estimation of lost circulation amount is necessary to find a solution. Lost circulation is influenced by different parameters such as mud weight, pump pressure, depth etc. Mud weight, pump pressure and flow rate of mud should be designed to prevent induced fractures and have the least amount of lost circulation. Artificial neural network is useful to find the relations of parameters with lost circulation. Genetic algorithm is applied on the achieved relations to determine the optimum mud weight, pump pressure, and flow rate. In an Iranian oil field, daily drilling reports of wells which are drilled using UBD technique are studied. Asmari formation is the most important oil reservoir of the studied field and UBD is used only in this interval. Three wells with the most, moderate and without lost circulation are chosen. In this article, the effect of mud weight, depth, pump pressure and flow rate of pump on lost circulation in UBD of Asmari formation in one of the Southwest Iranian fields is studied using drilling data and artificial neural network. In addition, the amount of lost circulation is predicted precisely with respect to two of the studied parameters using the presented correlations and the optimum mud weight, pump pressure and flow rate are calculated to minimize the lost circulation amount.http://www.sciencedirect.com/science/article/pii/S1110062116300575Real timeLost circulationUBDIranian reservoirNeural networkOptimization, Genetic algorithm
collection DOAJ
language English
format Article
sources DOAJ
author Pouria Behnoud far
Pantea Hosseini
spellingShingle Pouria Behnoud far
Pantea Hosseini
Estimation of lost circulation amount occurs during under balanced drilling using drilling data and neural network
Egyptian Journal of Petroleum
Real time
Lost circulation
UBD
Iranian reservoir
Neural network
Optimization, Genetic algorithm
author_facet Pouria Behnoud far
Pantea Hosseini
author_sort Pouria Behnoud far
title Estimation of lost circulation amount occurs during under balanced drilling using drilling data and neural network
title_short Estimation of lost circulation amount occurs during under balanced drilling using drilling data and neural network
title_full Estimation of lost circulation amount occurs during under balanced drilling using drilling data and neural network
title_fullStr Estimation of lost circulation amount occurs during under balanced drilling using drilling data and neural network
title_full_unstemmed Estimation of lost circulation amount occurs during under balanced drilling using drilling data and neural network
title_sort estimation of lost circulation amount occurs during under balanced drilling using drilling data and neural network
publisher Elsevier
series Egyptian Journal of Petroleum
issn 1110-0621
publishDate 2017-09-01
description Lost circulation can cause an increase in time and cost of operation. Pipe sticking, formation damage and uncontrolled flow of oil and gas may be consequences of lost circulation. Dealing with this problem is a key factor to conduct a successful drilling operation. Estimation of lost circulation amount is necessary to find a solution. Lost circulation is influenced by different parameters such as mud weight, pump pressure, depth etc. Mud weight, pump pressure and flow rate of mud should be designed to prevent induced fractures and have the least amount of lost circulation. Artificial neural network is useful to find the relations of parameters with lost circulation. Genetic algorithm is applied on the achieved relations to determine the optimum mud weight, pump pressure, and flow rate. In an Iranian oil field, daily drilling reports of wells which are drilled using UBD technique are studied. Asmari formation is the most important oil reservoir of the studied field and UBD is used only in this interval. Three wells with the most, moderate and without lost circulation are chosen. In this article, the effect of mud weight, depth, pump pressure and flow rate of pump on lost circulation in UBD of Asmari formation in one of the Southwest Iranian fields is studied using drilling data and artificial neural network. In addition, the amount of lost circulation is predicted precisely with respect to two of the studied parameters using the presented correlations and the optimum mud weight, pump pressure and flow rate are calculated to minimize the lost circulation amount.
topic Real time
Lost circulation
UBD
Iranian reservoir
Neural network
Optimization, Genetic algorithm
url http://www.sciencedirect.com/science/article/pii/S1110062116300575
work_keys_str_mv AT pouriabehnoudfar estimationoflostcirculationamountoccursduringunderbalanceddrillingusingdrillingdataandneuralnetwork
AT panteahosseini estimationoflostcirculationamountoccursduringunderbalanceddrillingusingdrillingdataandneuralnetwork
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