عنوان

Data-Driven Hydrocarbon Production Forecasting Using Machine Learning Techniques

Number

TLpq2330627608

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Data-Driven Hydrocarbon Production Forecasting Using Machine Learning Techniques

General Material Designation

[Thesis]

First Statement of Responsibility

Safari-Zanjani, Masoud

Subsequent Statement of Responsibility

Abdus Salam, Mohammad

Name of Publisher, Distributor, etc.

Southern University and Agricultural and Mechanical College

Date of Publication, Distribution, etc.

2019

Specific Material Designation and Extent of Item

100

Dissertation or thesis details and type of degree

M.S.

Body granting the degree

Southern University and Agricultural and Mechanical College

Text preceding or following the note

2019

Text of Note

Data analytics utilizes advanced statistical and machine learning methods to find the concealed information and trends present in different types of datasets. These methods have recently shown a great potential to solve the problems in oil and gas industry. The ability to find insights from large datasets can make an oil company more profitable and successful. Innovation of sophisticated artificial intelligence methods as well as new developments in powerful high-speed computing resources have made the machine learning techniques more powerful than ever. The oil and gas industry has been benefited from these algorithms and machine learning techniques have been applied to many petroleum engineering challenges. Artificial Neural Network (ANN), Linear Regression (LR), and Support Vector Regression (SVR) were employed in this thesis to forecast the daily oil production using Volve oil field production dataset. All three methods show a great potential for hydrocarbon production forecasting. Results for well NO159-F-1C, however, indicate that ANN had the best performance compare to other two methods. This doesn't mean that ANN is the superior method compare to LR and SVR in every situation. The performance of an algorithm must be examined for each specific case in order to select the best technique.

Computer science

Abdus Salam, Mohammad

Safari-Zanjani, Masoud

Electronic name

p

[Thesis]

276903

a

Y