Integrated Production Forecasting for Business Planning

2014 ◽  
Author(s):  
Peter Obidike ◽  
Chima Emelle ◽  
Rene Vlaardingerbroek
Keyword(s):  
Business Planning ◽  
Integrated Production ◽  
Production Forecasting

An Integrated Production Modelling workflow for CSG production forecasting and optimisation

2016 ◽  
Vol 34 ◽  
pp. 733-750
Author(s):  
A. Shields ◽  
S. Tihonova ◽  
R. Stott ◽  
L.A. Saputelli ◽  
Z. Haris ◽  
...  
Keyword(s):  
Integrated Production ◽  
Production Forecasting ◽  
Production Modelling

Integrated Production Modelling for CSG Production Forecasting

2015 ◽  
Author(s):  
A. Shields ◽  
S. Tihonova ◽  
R. Stott ◽  
L. A Saputelli ◽  
Z. Haris ◽  
...  
Keyword(s):  
Integrated Production ◽  
Production Forecasting ◽  
Production Modelling

Data and Model Integration Challenges for Integrated Production Forecasting-Experience from Super-Giant Onshore Fields

2019 ◽  
Author(s):  
Nagaraju Reddicharla ◽  
Rachelle Christine Cornwall ◽  
Erismar Rubio ◽  
Sandeep Soni ◽  
Jose Isambertt
Keyword(s):  
Model Integration ◽  
Integrated Production ◽  
Production Forecasting

Integrated Subsurface to Surface Modeling to Assess Reservoir Uncertainty Quantification to A Carbonate Reservoir

2020 ◽  
Author(s):  
A. Chaterine
Keyword(s):  
Uncertainty Quantification ◽  
Gas Production ◽  
Carbonate Reservoir ◽  
Production Volume ◽  
Production Forecast ◽  
Uncertainty Range ◽  
Field Development ◽  
Production Forecasting ◽  
Production Scheme ◽  
Rock Compressibility

This study accommodates subsurface uncertainties analysis and quantifies the effects on surface production volume to propose the optimal future field development. The problem of well productivity is sometimes only viewed from the surface components themselves, where in fact the subsurface component often has a significant effect on these production figures. In order to track the relationship between surface and subsurface, a model that integrates both must be created. The methods covered integrated asset modeling, probability forecasting, uncertainty quantification, sensitivity analysis, and optimization forecast. Subsurface uncertainties examined were : reservoir closure, regional segmentation, fluid contact, and SCAL properties. As the Integrated Asset Modeling is successfully conducted and a matched model is obtained for the gas-producing carbonate reservoir, highlights of the method are the following: 1) Up to ± 75% uncertainty range of reservoir parameters yields various production forecasting scenario using BHP control with the best case obtained is 335 BSCF of gas production and 254.4 MSTB of oil production, 2) SCAL properties and pseudo-faults are the most sensitive subsurface uncertainty that gives major impact to the production scheme, 3) EOS modeling and rock compressibility modeling must be evaluated seriously as those contribute significantly to condensate production and the field’s revenue, and 4) a proposed optimum production scenario for future development of the field with 151.6 BSCF gas and 414.4 MSTB oil that yields a total NPV of 218.7 MMUSD. The approach and methods implemented has been proven to result in more accurate production forecast and reduce the project cost as the effect of uncertainty reduction.

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