Assessment of reservoir uncertainties for development evaluation and risk analysis

Singh, Virendra; Hegazy, Mohamed; Fontanelli, L.

doi:10.1190/1.3104070

Cited by 12 publications

(4 citation statements)

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“…Appraisal must help manage residual uncertainty during the field life through economically viable interventions. The reservoir-specific uncertainties may be Hydrocarbon In-Place (HIP), recovery efficiency, fluid type and its quality, or a combination thereof (Lawrence et al, 2008;Singh et al, 2009;Rose, 2010;Nandurdikar and Wallace, 2011;Romundstad et al, 2013;Singh et al, 2013;Orellana et al, 2014). The appraisal framing and analysis workflow can be based on following considerations.…”

Section: Subsurface Appraisal Framing and Analysis Workflowmentioning

confidence: 99%

“…• Assess rock property averages using well data • Screen field data to determine suitable analogues related to key identified uncertainties • Probabilistic and model based volumetric analysis using ranges of key uncertainties to provide an initial range of volume potential for a field in terms of Inplace, recoverable and expected production rates (Singh et al, 2009;Wolff, Uncertainty in the possibility of different geological model should be fully captured. Risk is assessed using standard approach in order to facilitate the evaluation of the relative merits of appraisal well options and locations (Milkov, 2015).…”

Section: Post Discovery Resources Estimationmentioning

confidence: 99%

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Optimal Subsurface Appraisal: A Key Link to the Success of Development Projects-Few Examples

Singh¹,

Yemez²,

Izaguirre³

et al. 2017

American Journal of Applied Sciences

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During exploration and production phases, the life of a hydrocarbon field can be distinguished by five main stages: Exploration, appraisal, development, production and abandonment. Of particular interest is the appraisal stage, where billions of dollars are spent across the oil and gas industry on data gathering activities with a view to reducing subsurface uncertainties towards optimizing reservoir development and management. However, very limited attention is often paid to assessing the Value of Information (VOI) during data acquisition requirement planning, before requesting for such information. Appraisal, when conducted optimally, leads to informed sequential decisions resulting in a development plan that optimizes cost, hydrocarbon recovery and attempts to maximize Net Present Value (NPV). On the contrary, suboptimal appraisal, either in the form of under, or over appraisal, impacts project economics. Individual oil and gas companies use their own strategies, procedures and metrics to optimize subsurface appraisals and safeguard profitability and hence there are no industry standards. The capital intensiveness of the industry and emerging low oil price regime has necessitated scrutiny on every dollar spent on data gathering in the current business terrain. Appraisal should be optimized by way of case specific activity of data acquisition to ensure project deliverability under maximum residual uncertainties achieved by minimum appraisal cost. To ensure that the appraisal strategy is value driven, an effort has been made to briefly recapitulate the different components of appraisal strategy necessary for appraisal framing and evaluation workflow. The appraisal locations selection and their sequencing combines uncertainty reduction, probability of success with VOI technique to determine the number of appraisal wells, their sequence of drilling and their justification that is based on economic merit. The effectiveness of this systematic approach has been illustrated through some real field examples. This workflow along with periodic look-back analysis, during appraisal to development phases, has proven to be highly useful for (1) improving the understanding of geological model; (2) assess VOI from appraisal; (3) reduce subsurface uncertainties and (4) improve decision quality and whole cycle project economics.

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Section: Subsurface Appraisal Framing and Analysis Workflowmentioning

confidence: 99%

Section: Post Discovery Resources Estimationmentioning

confidence: 99%

Optimal Subsurface Appraisal: A Key Link to the Success of Development Projects-Few Examples

Singh¹,

Yemez²,

Izaguirre³

et al. 2017

American Journal of Applied Sciences

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“…To a certain extent, the advances in technology have been quite helpful in mitigating some of the key risks, but the risks are not yet fully reduced due to our inability to formulate a complete and precise description, and characterization of the reservoir leading to the uncertainty in our understating of reservoir description (Singh et al, 2009 and2013). Even though, a reservoir was deposited geologically and evolved into a unique hydrocarbonbearing entity, it cannot be deterministically defined or completely determined because of subsurface complexity and limited data.…”

Section: Introductionmentioning

confidence: 99%

Factors Contributing to Uncertain Production Forecasts: 3D Reservoir Modelling Perspectives

et al. 2014

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Finding and developing oil and gas assets has always been a risky business. The industry has a history of technological advances that have helped to reduce the risk. However, risk has not yet been fully reduced due to inherent uncertainties in the workflows used to generate production forecasts of the oil and gas fields using 3D reservoir models. Since, the reservoir properties vary spatially due to reservoir heterogeneities (occur at all scales, from pore scale to major reservoir units), to obtain reasonable production forecasts, an adequate understanding of the limitations imposed by the data, associated uncertainty, or the underlying geostatistical algorithms or approaches and their input requirements for the 3D reservoir models are absolutely necessary. Based on the lessons learned from 3D reservoir modelling studies performed in-house in different projects, available public domain literature, authors' and industry experiences, some of the identified key factors affecting production forecasts are: sparse and nonrepresentative data, biased estimates of Original Hydrocarbon In-Place, non-representative inputs distribution in the reservoir models due to lack of conceptual geologic model, inadequate static and dynamic models, poor use of seismic data, use of improper analogs, non-unique history matching calibration processes for brownfields and inappropriate use of uncertainty workflows and tools. To demonstrate and quantify the impact of different key factors under uncertainty which affect Hydrocarbon-In-Place, recoverable resources and production forecasts, using real field data for a clastic reservoir, a 3D static reservoir model was built using appropriate geo-statistical techniques and closed feedback loop between 3D static and dynamic models. Finally, the results are discussed which indicate that the evolution of modelling process will continue as new techniques/technologies are developed and implemented. This will enhance our ability to capture the physical realities of the real subsurface world, generate better production forecasts to reduce the risk associated with field developments.

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“…Singh et al 2009, Thore et al 2002. The risks and high investments associated with appraising and developing a deepwater reservoir make reliable quantification of these uncertainties a fundamental step of the risk analysis and ultimately the decision making (Charles et al 2001).…”

Section: Introductionmentioning

confidence: 99%

A Workflow to Quantify and Model Structural Uncertainty: Application to a Deepwater Giant Reservoir, Offshore Mozambique

Luciani¹,

Avella²

2014

Proceedings

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SUMMARYThe uncertainties related to structural seismic interpretation and velocity modeling can have a significant impact on gas in place evaluation. The risks and high investments associated with appraising and developing a deep-water reservoir make reliable quantification of these uncertainties a fundamental step of the risk analysis and ultimately the decision making. These considerations are a key driver for the development plan of the deepwater Mamba Complex, a super giant gas field discovery in offshore northern Mozambique. The magnitude of the discovery and the rapid development planning introduce a series of technical challenges, including the evaluation and quantification of the structural uncertainty, and its integration in the reservoir model. This paper proposes a workflow that effectively integrates probabilistic and scenario-based approaches, aiming to mitigate the risk associated with the development of the Mamba complex, and to help the decision maker.

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Assessment of reservoir uncertainties for development evaluation and risk analysis

Cited by 12 publications

References 0 publications

Optimal Subsurface Appraisal: A Key Link to the Success of Development Projects-Few Examples

Optimal Subsurface Appraisal: A Key Link to the Success of Development Projects-Few Examples

Factors Contributing to Uncertain Production Forecasts: 3D Reservoir Modelling Perspectives

A Workflow to Quantify and Model Structural Uncertainty: Application to a Deepwater Giant Reservoir, Offshore Mozambique

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