scholarly journals Spatial Distribution Prediction of Oil and Gas Based on Bayesian Network with Case Study

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Hongjia Ren ◽  
Xianchang Wang ◽  
Hongbo Ren ◽  
Qiulin Guo
Keyword(s):  
Spatial Distribution ◽  
Bayesian Network ◽  
Oil And Gas ◽  
Structure Learning ◽  
Interpolation Method ◽  
Probability Map ◽  
Bayesian Network Structure ◽  
Promising Target ◽  
Oil And Gas Resources ◽  
Gas Fields

Effectively predicting the spatial distribution of oil and gas contributes to delineating promising target areas for further exploration. Determining the location of hydrocarbon is a complex and uncertain decision problem. This paper proposes a method for predicting the spatial distribution of oil and gas resource based on Bayesian network. In this method, qualitative dependency relationship between the hydrocarbon occurrence and key geologic factors is obtained using Bayesian network structure learning by integrating the available geoscience information and the current exploration results and then using Bayesian network topology structure to predict the probability of hydrocarbon occurrence in the undiscovered area; finally, the probability map of hydrocarbon-bearing is formed by interpolation method. The proposed method and workflow are further illustrated using an example from the Carboniferous Huanglong Formation (C2hl) in the eastern part of the Sichuan Basin in China. The prediction results show that the coincidence rate between the results of 248 known exploration wells and the predicted results reaches 89.5%, and it has been found that the gas fields are basically located in the high value area of the hydrocarbon-bearing probability map. The application results show that the Bayesian network method can effectively predict the spatial distribution of oil and gas resources, thereby reducing exploration risks, optimizing exploration targets, and improving exploration benefits.

scholarly journals MANAGEMENT AUTHORITY OF OIL AND GAS RESOURCES IN ACEH TERRITORY AFTER SIGNING THE GOVERNMENT REGULATION NUMBER 23 YEAR 2015

2018 ◽  
Vol 3 (Number 2) ◽  
pp. 59-69
Author(s):  
Azhari Yahya ◽  
Nurdin MH
Keyword(s):  
Government Regulation ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Oil And Gas Fields ◽  
Oil And Gas Resources ◽  
Free Aceh Movement ◽  
The Government ◽  
Gas Fields ◽  
Energy Law

The oil and gas industry in Indonesia has been started since 1871 by Royal Dutch Shell. Meanwhile, the oil and gas industry in Aceh began in 1971 which was marked by the discovery of the Arun oil and gas fields. At that time, the management of oil and gas is done centrally by not involving the Government of Aceh as a regional producer. This led to armed conflict between the Government of Indonesia and the Free Aceh Movement and prolonged conflict (for 32 years) ended with the approval of the joint oil and gas management pattern found in the territory of Aceh as stipulated in the MoU Helsinki on August 15 2005, Law No. 11 of 2006 concerning the Government of Aceh and Government Regulation No. 23 of 2015 concerning Joint Management of Oil and Gas in Aceh. In order to finalize joint oil and gas management in Aceh, universities, especially the Faculty of Law, need to immediately prepare human resources who are competent in the oil and gas and energy law so that they are skilled at negotiating and drafting a Production Sharing Contracts (PSC) for oil and gas or Kontrak Bagi Hasil (KBH). For this purpose, law faculties need to immediately incorporate oil and gas and energy law courses into their curriculum.

An Algorithm for Learning the Skeleton of Large Bayesian Network

2015 ◽  
Vol 24 (04) ◽  
pp. 1550012
Author(s):  
Yanying Li ◽  
Youlong Yang ◽  
Wensheng Wang ◽  
Wenming Yang
Keyword(s):  
Bayesian Network ◽  
Structure Learning ◽  
State Of The Art ◽  
Large Network ◽  
Dependency Analysis ◽  
Learning From Data ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning ◽  
Np Hard Problem ◽  
Key Steps

It is well known that Bayesian network structure learning from data is an NP-hard problem. Learning a correct skeleton of a DAG is the foundation of dependency analysis algorithms for this problem. Considering the unreliability of the high order condition independence (CI) tests and the aim to improve the efficiency of a dependency analysis algorithm, the key steps are to use less number of CI tests and reduce the sizes of condition sets as many as possible. Based on these analyses and inspired by the algorithm HPC, we present an algorithm, named efficient hybrid parents and child (EHPC), for learning the adjacent neighbors of every variable. We proof the validity of the algorithm. Compared with state-of-the-art algorithms, the experimental results show that EHPC can handle large network and has better accuracy with fewer number of condition independence tests and smaller size of conditioning set.

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