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Case Study of Heterogeneity Index’s Effect on The Successful Workover Based on The Apriori Algorithm

Scientific Contributions Oil and Gas · Vol. 48 No. 1 (2025) · hlm. 135–144

DOI
10.29017/scog.v48i1.1658
ISSN
2541-0520, 2089-3361
Terbit
2025-03-07
Penerbit
Testing Center for Oil and Gas LEMIGAS
Bahasa
eng
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

The Indonesian government has set a target to reduce the consumption-production gap by increasing national oil production to 1 million barrels of oil per day (BOPD) and 12 billion standard cubic feet per day (BSCFD) of gas by 2030. Amongst several approaches, the optimization of mature fields offers a significant opportunity for quick production gains. However, analyzing these fields presents challenges due to the complexity, incompleteness, and poor quality of historical data. Heterogeneity Index (HI) is one of the methods that quickly measure well performance. This method is as simple as measuring a certain well as compared to the average performance at certain time. The parameter being used might vary, but production data is the most frequent one given its availability. Despite simple and practical, skepticism on the reliability of this method is still questionable. This work revisited "XYZ" field consisting of XX wells producing more than 32 years with hundreds of workovers. We brought evidences and insights on how HI leads to the workover success from. Apriori algorithm, an Association Rule Mining (ARM) technique, is employed to uncover rules from the noisy data. The results show that workover on wells with low HI mostly leads to success. Another insight is that of scale treatment is the most influential one in determining the success. Given these findings, the flow efficiency is the issue that should be well treated and HI is representative enough to measure this one.

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.lemigas.esdm.go.id/index.php/SCOG/oai, pertama di-harvest 10 Nov 2026, terakhir terlihat 10 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Case Study of Heterogeneity Index’s Effect on The Successful Workover Based on The Apriori Algorithm</dc:title>
	<dc:creator xml:lang="en">Maulana, Fahrizal</dc:creator>
	<dc:creator xml:lang="en">Yasutra, Amega</dc:creator>
	<dc:creator xml:lang="en">Syihab, Zuher</dc:creator>
	<dc:subject xml:lang="en">heterogeneity index</dc:subject>
	<dc:subject xml:lang="en">mature field</dc:subject>
	<dc:subject xml:lang="en">production optimization</dc:subject>
	<dc:subject xml:lang="en">Apriori algorithm</dc:subject>
	<dc:subject xml:lang="en">association rule mining</dc:subject>
	<dc:description xml:lang="en">The Indonesian government has set a target to reduce the consumption-production gap by increasing national oil production to 1 million barrels of oil per day (BOPD) and 12 billion standard cubic feet per day (BSCFD) of gas by 2030. Amongst several approaches, the optimization of mature fields offers a significant opportunity for quick production gains. However, analyzing these fields presents challenges due to the complexity, incompleteness, and poor quality of historical data. Heterogeneity Index (HI) is one of the methods that quickly measure well performance. This method is as simple as measuring a certain well as compared to the average performance at certain time. The parameter being used might vary, but production data is the most frequent one given its availability. Despite simple and practical, skepticism on the reliability of this method is still questionable. This work revisited "XYZ" field consisting of XX wells producing more than 32 years with hundreds of workovers. We brought evidences and insights on how HI leads to the workover success from. Apriori algorithm, an Association Rule Mining (ARM) technique, is employed to uncover rules from the noisy data. The results show that workover on wells with low HI mostly leads to success. Another insight is that of scale treatment is the most influential one in determining the success. Given these findings, the flow efficiency is the issue that should be well treated and HI is representative enough to measure this one.</dc:description>
	<dc:publisher xml:lang="en">Testing Center for Oil and Gas LEMIGAS</dc:publisher>
	<dc:date>2025-03-07</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.lemigas.esdm.go.id/index.php/SCOG/article/view/1658</dc:identifier>
	<dc:identifier>10.29017/scog.v48i1.1658</dc:identifier>
	<dc:source xml:lang="en">Scientific Contributions Oil and Gas; Vol. 48 No. 1 (2025); 135-144</dc:source>
	<dc:source xml:lang="id">Scientific Contributions Oil and Gas; Vol 48 No 1 (2025); 135-144</dc:source>
	<dc:source>2541-0520</dc:source>
	<dc:source>2089-3361</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.lemigas.esdm.go.id/index.php/SCOG/article/view/1658/1360</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 © Copyright by Authors. Published by LEMIGAS</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>