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The utilization of machine learning method to predict hydrocarbon flowrate for a better reservoir potential evaluation

The numbers of machine learning technologies used in subsurface characterization work is increasing with more company rely on data driven to assist in performing any evaluation. In this study, a supervised random forest machine learning approach was utilized in two stages; first stage was to predict...

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Main Authors:	Kasim, Fadzlin Hasani, Idris, Nurul Nadhira, Saeed Majidaie, Saeed Majidaie, Kantaatmadja, Budi Priyatna, Ahmed Siddiqui, Numair, Sidek, Akhmal, Yahaya, Nur Aqilah Nabila
Format:	Conference or Workshop Item
Published:	2022
Subjects:	Q Science (General)
Online Access:	http://eprints.utm.my/108685/ http://dx.doi.org/10.2523/IPTC-22025-MS
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id	my.utm.108685
record_format	eprints
spelling	my.utm.1086852024-11-27T09:18:47Z http://eprints.utm.my/108685/ The utilization of machine learning method to predict hydrocarbon flowrate for a better reservoir potential evaluation Kasim, Fadzlin Hasani Idris, Nurul Nadhira Saeed Majidaie, Saeed Majidaie Kantaatmadja, Budi Priyatna Ahmed Siddiqui, Numair Sidek, Akhmal Yahaya, Nur Aqilah Nabila Q Science (General) The numbers of machine learning technologies used in subsurface characterization work is increasing with more company rely on data driven to assist in performing any evaluation. In this study, a supervised random forest machine learning approach was utilized in two stages; first stage was to predict static reservoir using well logs and core as inputs. The output is then used as the basis in the second stage to predict initial oil rate (Qi) and subsequently to determine estimated ultimate recovery (EUR) at targeted interval as proposed in the first stage. Static reservoir machine learning prediction outputs were benchmark with available routine core analysis with the result showed R2 of 88% respectively. For initial oil rate (Qi) prediction, a total of 9000 observation points from 20 wells were extracted for training and blind testing process by using variables such as permeability, net thickness, well choke size, well flowing pressure, average pressure, water cut, irreducible water saturation (Swi), and historical production rate. The estimated ultimate recovery (EUR) is then predicted utilizing the thickness of that unit and the decline rate that is obtained from the neighboring wells that has produced from the said reservoir as the analogue. The Qi and EUR results from machine learning is compared with the estimated Qi and EUR using conventional methods for verification purpose. The results from machine learning dynamic properties prediction showed 97% R2 for training while the testing score mean is 87% against the historical data. High R2 from static and dynamic machine learning prediction indicated that the method was reliable and able to assist petroleum engineer in reservoir potential evaluation process. 2022-02 Conference or Workshop Item PeerReviewed Kasim, Fadzlin Hasani and Idris, Nurul Nadhira and Saeed Majidaie, Saeed Majidaie and Kantaatmadja, Budi Priyatna and Ahmed Siddiqui, Numair and Sidek, Akhmal and Yahaya, Nur Aqilah Nabila (2022) The utilization of machine learning method to predict hydrocarbon flowrate for a better reservoir potential evaluation. In: International Petroleum Technology Conference, 21 February 2022 - 23 February 2022, Riyadh, Saudi Arabia. http://dx.doi.org/10.2523/IPTC-22025-MS
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	Q Science (General)
spellingShingle	Q Science (General) Kasim, Fadzlin Hasani Idris, Nurul Nadhira Saeed Majidaie, Saeed Majidaie Kantaatmadja, Budi Priyatna Ahmed Siddiqui, Numair Sidek, Akhmal Yahaya, Nur Aqilah Nabila The utilization of machine learning method to predict hydrocarbon flowrate for a better reservoir potential evaluation
description	The numbers of machine learning technologies used in subsurface characterization work is increasing with more company rely on data driven to assist in performing any evaluation. In this study, a supervised random forest machine learning approach was utilized in two stages; first stage was to predict static reservoir using well logs and core as inputs. The output is then used as the basis in the second stage to predict initial oil rate (Qi) and subsequently to determine estimated ultimate recovery (EUR) at targeted interval as proposed in the first stage. Static reservoir machine learning prediction outputs were benchmark with available routine core analysis with the result showed R2 of 88% respectively. For initial oil rate (Qi) prediction, a total of 9000 observation points from 20 wells were extracted for training and blind testing process by using variables such as permeability, net thickness, well choke size, well flowing pressure, average pressure, water cut, irreducible water saturation (Swi), and historical production rate. The estimated ultimate recovery (EUR) is then predicted utilizing the thickness of that unit and the decline rate that is obtained from the neighboring wells that has produced from the said reservoir as the analogue. The Qi and EUR results from machine learning is compared with the estimated Qi and EUR using conventional methods for verification purpose. The results from machine learning dynamic properties prediction showed 97% R2 for training while the testing score mean is 87% against the historical data. High R2 from static and dynamic machine learning prediction indicated that the method was reliable and able to assist petroleum engineer in reservoir potential evaluation process.
format	Conference or Workshop Item
author	Kasim, Fadzlin Hasani Idris, Nurul Nadhira Saeed Majidaie, Saeed Majidaie Kantaatmadja, Budi Priyatna Ahmed Siddiqui, Numair Sidek, Akhmal Yahaya, Nur Aqilah Nabila
author_facet	Kasim, Fadzlin Hasani Idris, Nurul Nadhira Saeed Majidaie, Saeed Majidaie Kantaatmadja, Budi Priyatna Ahmed Siddiqui, Numair Sidek, Akhmal Yahaya, Nur Aqilah Nabila
author_sort	Kasim, Fadzlin Hasani
title	The utilization of machine learning method to predict hydrocarbon flowrate for a better reservoir potential evaluation
title_short	The utilization of machine learning method to predict hydrocarbon flowrate for a better reservoir potential evaluation
title_full	The utilization of machine learning method to predict hydrocarbon flowrate for a better reservoir potential evaluation
title_fullStr	The utilization of machine learning method to predict hydrocarbon flowrate for a better reservoir potential evaluation
title_full_unstemmed	The utilization of machine learning method to predict hydrocarbon flowrate for a better reservoir potential evaluation
title_sort	utilization of machine learning method to predict hydrocarbon flowrate for a better reservoir potential evaluation
publishDate	2022
url	http://eprints.utm.my/108685/ http://dx.doi.org/10.2523/IPTC-22025-MS
_version_	1817841631438569472
score	13.223943

The utilization of machine learning method to predict hydrocarbon flowrate for a better reservoir potential evaluation

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