Integrated Thermodynamic Reservoir Modeling through Efficient Design of Experiments for Optimal Field Development through Steamflooding Processes

Abstract Design of Experiments (DoE) is one of the most commonly employed techniques in the petroleum industry for Assisted History Matching (AHM) and uncertainty analysis of reservoir production forecasts. Although conceptually straightforward, DoE is often misused by practitioners because many of its statistical and modeling principles are not carefully followed. Our earlier paper (Li et al. 2019) detailed the best practices in DoE-based AHM for brownfields. However, to our best knowledge, there is a lack of studies that summarize the common caveats and pitfalls in DoE-based production forecast uncertainty analysis for greenfields and history-matched brownfields. Our objective here is to summarize these caveats and pitfalls to help practitioners apply the correct principles for DoE-based production forecast uncertainty analysis. Over 60 common pitfalls in all stages of a DoE workflow are summarized. Special attention is paid to the following critical project transitions: (1) the transition from static earth modeling to dynamic reservoir simulation; (2) from AHM to production forecast; and (3) from analyzing subsurface uncertainties to analyzing field-development alternatives. Most pitfalls can be avoided by consistently following the statistical and modeling principles. Some pitfalls, however, can trap experienced engineers. For example, mistakes made in handling the three abovementioned transitions can yield strongly unreliable proxy and sensitivity analysis. For the representative examples we study, they can lead to having a proxy R2 of less than 0.2 versus larger than 0.9 if done correctly. Two improved experimental designs are created to resolve this challenge. Besides the technical pitfalls that are avoidable via robust statistical workflows, we also highlight the often more severe non-technical pitfalls that cannot be evaluated by measures like R2. Thoughts are shared on how they can be avoided, especially during project framing and the three critical transition scenarios.

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Sour Gas Field Development with Integrated 3d Reservoir Modeling and Uncertainty Analysis

Springer Series in Geomechanics and Geoengineering - Proceedings of the International Field Exploration and Development Conference 2020 ◽

10.1007/978-981-16-0761-5_10 ◽

2021 ◽

pp. 102-113

Author(s):

Yue Yu ◽

Minh Van Vo ◽

Jian-jiang Lv

Keyword(s):

Uncertainty Analysis ◽

Reservoir Modeling ◽

Gas Field ◽

Field Development ◽

Sour Gas

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Seismic AVO Inversion for Optimal Reservoir Modeling and Field Development, Offshore Abu Dhabi, UAE

10.1190/segj2018-148.1 ◽

2019 ◽

Author(s):

Hiroki Miyamoto ◽

Toshiaki Shibasaki ◽

Samir Bellah ◽

Sami Al Jasmi

Keyword(s):

Abu Dhabi ◽

Reservoir Modeling ◽

Field Development ◽

Avo Inversion

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An Efficient Design of Experiments Using RBD-Fast

10.23967/wccm-eccomas.2020.057 ◽

2021 ◽

Author(s):

S. Wenzel ◽

E. Slomski-Vetter ◽

T. Melz

Keyword(s):

Design Of Experiments ◽

Efficient Design

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Statistical Design of Experiments: An introductory case study for polymer composites manufacturing applications

MATEC Web of Conferences ◽

10.1051/matecconf/202134700028 ◽

2021 ◽

Vol 347 ◽

pp. 00028

Author(s):

Natasha Botha ◽

Helen M. Inglis ◽

Roelof Coetzer ◽

F. Johan W.J. Labuschagne

Keyword(s):

Optimal Design ◽

Design Of Experiments ◽

Polymer Composites ◽

Response Surface ◽

Statistical Design ◽

Efficient Design ◽

Statistical Design Of Experiments ◽

Wide Range ◽

Multiple Variables

Statistical design of experiments (DoE) aims to develop a near efficient design while minimising the number of experiments required. This is an optimal approach especially when there is a need to investigate multiple variables. DoE is a powerful methodology for a wide range of applications, from the efficient design of manufacturing processes to the accurate evaluation of global optima in numerical studies. The contribution of this paper is to provide a general introduction to statistical design of experiments for a non-expert audience, with the aim of broadening exposure in the applied mechanics community. We focus on response surface methodology (RSM) designs — Taguchi Design, Central Composite Design, Box-Behnken Design and D-optimal Design. These different RSM designs are compared in the context of a case study from the field of polymer composites. The results demonstrate that an exact D-optimal design is generally considered to be a good design when compared to the global D-optimal design. That is, it requires fewer experiments while retaining acceptable efficiency measures for all three response surface models considered. This paper illustrates the benefits of DoE, demonstrates the importance of evaluating different designs, and provides an approach to choose the design best suited for the problem of interest.

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Efficient design of experiments in the Monod model

Journal of the Royal Statistical Society Series B (Statistical Methodology) ◽

10.1111/1467-9868.00412 ◽

2003 ◽

Vol 65 (3) ◽

pp. 725-742 ◽

Cited By ~ 15

Author(s):

Holger Dette ◽

Viatcheslav B. Melas ◽

Andrey Pepelyshev ◽

Nikolai Strigul

Keyword(s):

Design Of Experiments ◽

Monod Model ◽

Efficient Design

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Facies-based seismic inversion for a field development plan

The Leading Edge ◽

10.1190/tle38100770.1 ◽

2019 ◽

Vol 38 (10) ◽

pp. 770-779

Author(s):

Ehsan Zabihi Naeini ◽

Jalil Nasseri

Keyword(s):

Seismic Inversion ◽

Thin Layers ◽

Reservoir Modeling ◽

Inversion Method ◽

Technical Solution ◽

Hydrocarbon Production ◽

Field Development ◽

Development Plans ◽

Central North Sea ◽

Probabilistic Nature

Field appraisal and development plans aim to provide the best technical solution for optimizing hydrocarbon production and require integration between various disciplines including geology, geophysics, engineering, well planning, and environmental sciences. Seismic inversion could provide one essential component for reservoir modeling in support of appraisal and development evaluations. Therefore, it is important to quantitatively assess all of the possibilities and uncertainties involved in reservoir definition and extension. A probabilistic facies-based seismic inversion method has been utilized to achieve this goal in a recent Central North Sea discovery. The probabilistic nature of the inversion allows computation of various scenarios. We categorically selected, among others, most likely, optimistic, and pessimistic scenarios based on prior knowledge and calibration at the wells. Then, we performed a statistical analysis of all of the scenarios to identify the uncertainties. We also performed a postinversion forward-modeling study to assess uncertainties that may be related to thin layers of subseismic resolution.

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