Experience with the Quantification of Subsurface Uncertainties
- T. Charles (TotalFina Elf) | J.M. Guemene (TotalFina Elf) | B. Corre (TotalFina Elf) | G. Vincent (TotalFina Elf) | O. Dubrule (TotalFina Elf)
- Document ID
- Society of Petroleum Engineers
- SPE Asia Pacific Oil and Gas Conference and Exhibition, 17-19 April, Jakarta, Indonesia
- Publication Date
- Document Type
- Conference Paper
- 2001. Society of Petroleum Engineers
- 4.1.2 Separation and Treating, 5.1.8 Seismic Modelling, 5.5.11 Formation Testing (e.g., Wireline, LWD), 5.1.5 Geologic Modeling, 1.6 Drilling Operations, 5.6.9 Production Forecasting, 5.1.1 Exploration, Development, Structural Geology, 5.1.2 Faults and Fracture Characterisation, 5.1.3 Sedimentology, 5.1.7 Seismic Processing and Interpretation, 5.3.4 Integration of geomechanics in models, 4.1.5 Processing Equipment, 5.5.5 Evaluation of uncertainties
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Now that tools are available for building 3D earth models, and for quantifying uncertainties on parameters associated with the earth model, it is much easier to understand the impact of all subsurface uncertainties on field production. In the last years, we have accumulated a significant experience and know-how addressing this sort of issues in integrated studies.
We briefly recall how we address the problems, what tools and methodologies are used for quantifying the joint impact of subsurface uncertainties. We discuss the key points in a study :
clearly stressing which operational decision will be supported by the uncertainty study.
reformulating the problem in terms of an uncertainty study workflow.
listing the key subsurface uncertainties impacting the study.
translating these key uncertainties into uncertainties on gross-rock volume, oil-in-place or reserves, using the workflow.
Then we illustrate the approach with four field cases. The first example highlights how the joint impact of geometrical, geological and dynamic uncertainties on reserves estimates can be quantified. The second example focusses on the impact of fault sealing on a development decision for an HP-HT field. The third example shows how uncertainty figures can be used to help optimally locate an appraisal well. The last example illustrates how the quantification of structural uncertainties can be used to justify the acquisition or processing of seismic data. We also discuss how new uncertainty quantifications techniques are impacting some of the standard industry workflows. For instance :
scenario approaches can now be run in conjunction with probabilistic approaches.
rather than defining three mini, median and maxi geological models, we prefer to define these models in relation with quantitative parameters of interest, such as OIP and Reserves.
We also stress that all our approach merely consists of translating uncertainties on input parameters into uncertainties on parameters of economic interest. The quality of the result obtained depends on the validity of quantified uncertainties on input parameters.
Reservoir modelling is a challenging task for two main reasons :
the complexity of the physics involved in predicting flow
the lack of data available for modelling the reservoir
This data shortage is mainly due to reservoir complexity and to the high cost of data acquisition. Therefore, the prediction of oil-in-place, production profiles, or ultimate recovery, are difficult exercice, and any forecast figure is uncertain. A systematic quantification of technical risks is crucial for decision makers to be able to compare or rank possible projects of a global portfolio, or understand the risk level they are taking on any new developement1,2. A quantification of the impact of various subsurface uncertainties on economic figures may also help justify the acquisition of further data, in order to reduce the uncertainty before major decisions are made.
A chain of tools has been developped at TotalFinaElf to deal with the quantification of subsurface uncertainties. This chain uses as its backbone three main softwares, each dealing with specific subsurface uncertainties3:
ALEA : in-house software developed in the GOCAD4 environment, which allows the quantification of the joint impact of seismic-picking, time-to-depth conversion uncertainties and any other uncertainty in seismic parameters on Gross Rock Volume uncertainties5,6. Based on uncertainty maps produced by seismic interpreteters, ALEA simulates a large number of structural models of the reservoir, calculates the corresponding Rock Volumes and exports these surfaces towards JACTA™.
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