Integration of Time-Lapse Seismic and Production Data in A Gulf of Mexico Gas Field

2000 ◽  
Author(s):  
Xuri Huang ◽  
Robert Will ◽  
Mashiur Khan ◽  
Larry Stanley
Keyword(s):  
Gulf Of Mexico ◽  
Time Lapse ◽  
Production Data ◽  
Gas Field

Integration of time-lapse seismic and production data in a Gulf of Mexico gas field

2001 ◽  
Vol 20 (3) ◽  
pp. 278-289 ◽  
Author(s):  
Xuri Huang ◽  
Robert Will ◽  
Mashiur Khan ◽  
Larry Stanley
Keyword(s):  
Gulf Of Mexico ◽  
Time Lapse ◽  
Production Data ◽  
Gas Field

Reconciling Time-Lapse Seismic and Production Data Using Streamline Models: The Bay Marchand Field, Gulf of Mexico

2003 ◽  
Author(s):  
D.W. Vasco ◽  
Akhil Datta-Gupta ◽  
Zhong He ◽  
Ronald Behrens ◽  
James Rickett ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Time Lapse ◽  
Production Data

Reconciling Time-lapse Seismic Data and Production Data: Application to a Gas Field

1999 ◽  
Author(s):  
Xuri Huang ◽  
Robert Will ◽  
Mashiur Khan ◽  
Larry Stanley
Keyword(s):  
Seismic Data ◽  
Time Lapse ◽  
Production Data ◽  
Gas Field ◽  
Data Application

scholarly journals Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1052
Author(s):  
Baozhong Wang ◽  
Jyotsna Sharma ◽  
Jianhua Chen ◽  
Patricia Persaud
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Reservoir Characterization ◽  
Time Lapse ◽  
Production Data ◽  
Oil Saturation ◽  
Ensemble Machine Learning ◽  
Input Parameters ◽  
Saturation Profiles ◽  
Field Production

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

Analysis of time-lapse seismic and production data for reservoir model classification and assessment

2018 ◽  
Vol 15 (4) ◽  
pp. 1561-1587 ◽  
Author(s):  
Rafael Souza ◽  
David Lumley ◽  
Jeffrey Shragge ◽  
Alessandra Davolio ◽  
Denis José Schiozer
Keyword(s):  
Time Lapse ◽  
Production Data ◽  
Reservoir Model ◽  
Model Classification

GAS FIELD DELIVERABILITY PREDICTIONS AND DEVELOPMENT ECONOMICS

1967 ◽  
Vol 7 (1) ◽  
pp. 115
Author(s):  
A. N. Edgington ◽  
N. E. Cleland
Keyword(s):  
Material Balance ◽  
Production Data ◽  
Reservoir Pressure ◽  
Computer Programme ◽  
Gas Field ◽  
Open Flow ◽  
Bottom Hole ◽  
Present Worth ◽  
Net Revenue ◽  
Economic Criteria

Forecast of well deliverabilities are an absolute necessity for the realistic planning of the production, transmission and reticulation of natural gas.Gas well deliverability is a function of both natural and artificial limitations and both must be considered in a deliverability forecast.The direct prediction of the decline in wellhead deliverability during the life of a well is a relatively recent development and uses a wellhead relationship analogous to the formation open flow formula. This relationship, combined with the material balance pressure decline equation and the formula relating bottom-hole to wellhead conditions, forms the basis for deliverability forecasts.Compression is added to provide maximum well deliverability and wells may be drilled during the life of a project to maintain deliverability. New wells should meet certain minimum economic criteria before they can be justified. Suggested Criteria are:The net revenue to be earned by the new well must be a pre-selected multiple of the investment required,The present worth of the net revenue discounted at a pre-selected rate must be greater than the investment required.A computer programme has been written to carry out the tedious, repetitive and time-consuming calculations which are necessary for the solution to the problem of deliverability forecasting. This programme calculates the annual production and availability of pipeline gas as well as the number of welJs required to deplete the reserves efficiently. The average reservoir pressure and shut-in and flowing wellhead pressures are forecast and the amount of compression required is calculated. The computer output includes all the production data required for a complete economic analysis of a project involving the depletion of a gas field.

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