Managing Reservoir Uncertainty in Gas Field Development Using Experimental Design

Itotoi, Ibi-Ada; Ojeke, Akpoebi; Nnamdi, Dike; Umurhohwo, Jonathan; Osaigbovo, Benjamin; Kanu, Akachidike

doi:10.2118/140619-ms

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…Experimental design is a structured method used to evaluate relationship between the different factors affecting a process to the output of the process. It is widely used in statistics study, but also in petroleum engineering study as shown by Itotoi et al (2010) and Kalla, White, et al (2005). Among different method of experimental design, the one which is widely used in Oil and Gas engineering is the Full Factorial Design.…”

Section: Figure 1 P Ield Pipeline Network Mapmentioning

confidence: 99%

“…Bose and Kishen (1940) and Plackett and Burman (1946) provide information on how to de ine combination of factors in order to properly evaluate the relationship between them. Itotoi in his publication "Managing Reservoir Uncertainty in Gas Field Development Using Experimental Design" (Itotoi et al, 2010) demonstrate how to use experimental design to calculate the probability of Connected Gas In Place (CGIP) and Estimated Ultimate Reserves (EUR). The idea is using experimental design method to create a set of simulation inputs resulting in a response equation.…”

Section: Figure 1 P Ield Pipeline Network Mapmentioning

confidence: 99%

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Managing production profile uncertainties in P field LLP project economic evaluation using factorial design

2019

J. adm. bus. stud.

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This study evaluates a new approach to perform an economic evaluation for the Low Low Pressure (LLP) project on the different platforms. P ield is a Gas ield in the Mahakam area, East Kalimantan, Indonesia. Since 1999, its production has decreased and is forecasted to fall below economical cut-off starting at the end of 2020.project may extend the life of the ield. In 2016, An economic evaluation had been performed for the LLP pilot project on Platform 4 and Platform 5 but did not properly integrate the uncertainties behind the production pro ile. The new method proposed in this study develops a model from platform 2 data to capture production pro ile uncertainties by using factorial design. Monte Carlo simulation is applied to the model to obtain a production pro ile range. Economic analysis is then performed to calculate Net Present Value (NPV), Internal Rate of Return (IRR), and Payout Time (POT). Eventually, the new method concludes that the LLP project on Platform 2 will generate cumulative cash low between 32.9 to 60.4 MUSD (NPV0) or 11.7 to 21.8 MUSD (NPV11) with IRR ranging between 27 to 34%. Therefore it is economical. Information gathered from Platform 2 evaluation hinted that only Platform 1 has the potential to be economical. Additional work is required to have a complete economic evaluation. However, the management should decide to go on with the platform 2 LLP project.

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Section: Figure 1 P Ield Pipeline Network Mapmentioning

confidence: 99%

Section: Figure 1 P Ield Pipeline Network Mapmentioning

confidence: 99%

Managing production profile uncertainties in P field LLP project economic evaluation using factorial design

2019

J. adm. bus. stud.

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“…As part of his work he also explored the applicability of different regression equations to characterize reservoir models. Kabir et al (2004) and Itotoi et al (2010) also demonstrate experimental design applied to the development of green conventional fields using D-optimal and folded Plackett-Burman designs. Experimental design has also found applicability for fracture design, nodal analysis, and geostatistics [Zhang and Du (2011), Okenyi and Emeka (2012), White et al (2001), Friedmann et al (2001)].…”

Section: Experimental Design Applied To Reservoir Studiesmentioning

confidence: 99%

Addressing Forecasting Non-Uniqueness and Uncertainty in Unconventional Reservoir Systems Using Experimental Design

Collins¹,

Badessich²,

Ilk³

2015

Day 3 Wed, September 30, 2015

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Model-based production analysis using analytical or numerical models is not a new phenomenon and is considered a robust technique for analyzing and forecasting production data; however, its application to unconventional reservoir systems often proves problematic due to model non-uniqueness resulting from long-term transient flow regimes. This non-uniqueness, an unavoidable fact when analyzing inverse problems, is worsened by the uncertainty surrounding input model parameters when attempting to describe reservoir systems with a great deal of complexity (e.g. very low permeability, geomechanical effects, near-critical fluids, natural fracturing, etc.). The problem facing the engineer presents itself when different combinations of input parameters yield nearly identical history matches but very different time-rate profiles and estimated ultimate recovery (EUR) values when forecasting future production for a particular well.A systematic framework that covers the full range of uncertainty for all relevant input parameters would clearly mitigate the ambiguity of production analysis and forecasting under uncertain conditions. In this work it is proposed that experimental design, which is a statistical technique used to describe or optimize a process by systematically analyzing the effect of the various controllable and uncontrollable factors of a system on an output (e.g. EUR), can provide such a framework. In this work, a methodology combining model-based production analysis with experimental design is used to history match and forecast fractured vertical and multi-fractured horizontal oil wells in the Vaca Muerta Shale with high-frequency time-rate-pressure data. The primary objectives of this work are to provide a comprehensive overview of the Vaca Muerta shale, outline experimental design as it relates to model-based production analysis, quantify uncertainties in model input parameters, and finally history match and forecast two wells that are producing in the Vaca Muerta Shale.

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An Integrated Approach to Identifying and Reducing Reservoir Uncertainties in a Turbidite Deep Water Environment

et al. 2011

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Deep water developments have become increasingly challenging due to the heterogeneity of the reservoirs where complex architectures associated with turbidite deposition often results in reservoir compartmentalization and complex drainage pathways. These in most instances, result in sub-optimal reservoir development and sweep efficiency. Over the years, identifying and reducing the uncertainties associated with field development as a result of these compartmentalization and other complexities have been a major challenge to geologists and engineers in subsurface studies. This has necessitated the integrated application of high resolution 3D and 4D seismic, well logs, cores, latest techniques and concepts to narrow down the range of uncertainties for optimum reservoir management. To reduce the uncertainty in the depositional architecture of the Bonga field reservoirs, a holistic approach involving the loop scale seismic stratigraphy interpretation techniques, detailed static and dynamic modeling strategy was adopted to determine the reservoir properties. The understanding of sub-regional to reservoir scale seismic stratigraphy has been used in framing the geological constraints which forms the basis for building realistic static and dynamic models. Loop-scale seismic stratigraphy is used to extract the maximum information from the seismic data. The static reservoir models are generated by a hierarchical workflow where large scale structures such as the channel complexes are modeled first, then the internal geometries such as the channel storey sets and finally the reservoir properties are populated based on well logs, cores and reservoir analogues. These static reservoir models are taken into the dynamic simulator where the reservoir uncertainties are re-assessed based on matching the production history and 4D seismic. The dynamic reservoir modeling involves the use of Stochastic Uncertainty Management techniques incorporating the Design of Experiment with the objective of narrowing the range of uncertainties in the subsurface parameters. Integrating the static and dynamic modeling of the reservoirs provides a major improvement in our understanding of the subsurface by eliminating scenarios that do not honor all of the data available. This study has resulted in subsurface models that represent an improved understanding of reservoir property distribution and connectivity. These models are being used for improved reservoir management strategies and effective field development planning.

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Managing Reservoir Uncertainty in Gas Field Development Using Experimental Design

Cited by 11 publications

References 11 publications

Managing production profile uncertainties in P field LLP project economic evaluation using factorial design

Managing production profile uncertainties in P field LLP project economic evaluation using factorial design

Addressing Forecasting Non-Uniqueness and Uncertainty in Unconventional Reservoir Systems Using Experimental Design

An Integrated Approach to Identifying and Reducing Reservoir Uncertainties in a Turbidite Deep Water Environment

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