Construction of Analogy Indicator System and Machine-Learning-Based Optimization of Analogy Methods for Oilfield Development Projects

Muzhen Zhang (), Zhanxiang Lei (), Chengyun Yan, Baoquan Zeng, Fei Huang, Tailai Qu, Bin Wang and Li Fu
Additional contact information
Muzhen Zhang: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Zhanxiang Lei: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Chengyun Yan: The First Natural Gas Plant of PetroChina Qinghai Oilfield Company, Golmud 816000, China
Baoquan Zeng: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Fei Huang: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Tailai Qu: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Bin Wang: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Li Fu: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China

Energies, 2025, vol. 18, issue 15, 1-28

Abstract: Oil and gas development is characterized by high technical complexity, strong interdisciplinarity, long investment cycles, and significant uncertainty. To meet the need for quick evaluation of overseas oilfield projects with limited data and experience, this study develops an analogy indicator system and tests multiple machine-learning algorithms on two analogy tasks to identify the optimal method. Using an initial set of basic indicators and a database of 1436 oilfield samples, a combined subjective–objective weighting strategy that integrates statistical methods with expert judgment is used to select, classify, and assign weights to the indicators. This process results in 26 key indicators for practical analogy analysis. Single-indicator and whole-asset analogy experiments are then performed with five standard machine-learning algorithms—support vector machine (SVM), random forest (RF), backpropagation neural network (BP), k-nearest neighbor (KNN), and decision tree (DT). Results show that SVM achieves classification accuracies of 86% and 95% in medium-high permeability sandstone oilfields, respectively, greatly surpassing other methods. These results demonstrate the effectiveness of the proposed indicator system and methodology, providing efficient and objective technical support for evaluating and making decisions on overseas oilfield development projects.

Keywords: new oil and gas project evaluation; oilfield analogy indicators; machine learning; analogy method; asset optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/15/4076/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/15/4076/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:15:p:4076-:d:1714936

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().