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Structuring an Integrative Approach for Field Development Planning Using Artificial Intelligence and its Application to Tombua Landana Asset in Angola.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Structuring an Integrative Approach for Field Development Planning Using Artificial Intelligence and its Application to Tombua Landana Asset in Angola./
作者:	Ketineni, Sarath Pavan.
面頁冊數:	1 online resource (171 pages)
附註:	Source: Dissertations Abstracts International, Volume: 79-09, Section: B.
Contained By:	Dissertations Abstracts International79-09B.
標題:	Petroleum engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10759911click for full text (PQDT)
ISBN:	9780355652383

Structuring an Integrative Approach for Field Development Planning Using Artificial Intelligence and its Application to Tombua Landana Asset in Angola.
Ketineni, Sarath Pavan.

Structuring an Integrative Approach for Field Development Planning Using Artificial Intelligence and its Application to Tombua Landana Asset in Angola. - 1 online resource (171 pages)

Source: Dissertations Abstracts International, Volume: 79-09, Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2015.

Includes bibliographical references

Field development studies are at the forefront of common engineering practices in petroleum industry to maximize the returns on a given asset. In early stages of reservoir depletion, it is often a challenging task to accurately determine reservoir properties that are representative of the actual field. Reservoir modeling is the traditional way that engineers performed to develop field development and depletion plans. Due to different scales of data obtained from various sources like seismic data, well logs, cores, and production data, there is a lot of uncertainty in solving the inverse problem of estimating formation rock and fluid properties from the field data. Increase in complexity of formations and scarcity of reservoir data have made reservoir characterization a challenging task. Soft computing techniques have gained popularity in petroleum industry to identify complex patterns that exist between various reservoir data collected from multiple sources and be able to successfully characterize a reservoir. In this work, a work-flow is developed for devising a comprehensive reservoir characterization tool based on artificial neural network. A case study of Chevron's Tombua Landana Asset is used in demonstrating the tenets of the work-flow. The reservoir under consideration is highly heterogeneous in terms of property distribution and is believed to be highly channelized. The ANN based tool will assist in identifying sweet spots by predicting optimal well location/path/completion parameters and production schedule. The multilayer feed forward back propagation based neural network tool developed is able to capture the correlations that exist amongst seismic data, well logs, completion data, and production data. Well logs are correlated using surface seismic attributes and geometric location of wells with an average testing error of less than 15%. The range of testing errors is in between 1-30%. The tool enables the user to predict the entire well log suite for even a horizontal well of user defined configuration through a graphic user interface. Having correlated seismic data with well logs, synthetic well logs are generated for the entire area of seismic coverage. To predict production data, along with seismic data and well logs, schedule of production and interference factors are incorporated as functional links. Upon analyzing the relevancies of input data, functional links based on geographic location and injection wells are included to make the prediction more reliable and robust. Production performance networks comprising cumulative oil, gas and water production performance prediction modules are developed to forecast performance of wells at undrilled locations. Oil networks indicated an average error of 21% in blind testing cases. Highly variable gas production could also be correlated with the seismic data and well log data within 32% error. Water production networks indicated a high error of 46% on blind testing cases. Oil, gas and water production forecast maps are generated using production performance networks. Maps generated indicate flow paths that exist in the field. Monte Carlo simulations are performed to predict P10/P50/P90 OOIP maps. The developed model enables reservoir engineers to construct well paths based on synthetic log cubes generated in conjunction with Monte Carlo OOIP estimates. Genetic algorithms can be used to optimize selection of new well locations and well paths enabling production from sweet spots. Further analysis using NPV (net present value) calculations is integrated with production predictions to identify the potential producer locations and well paths.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9780355652383Subjects--Topical Terms:

566616
Petroleum engineering.
Subjects--Index Terms:

artificial neural networksIndex Terms--Genre/Form:

542853
Electronic books.

Structuring an Integrative Approach for Field Development Planning Using Artificial Intelligence and its Application to Tombua Landana Asset in Angola.
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