Integrated 3D reservoir interpretation and modeling: Lessons learned and proposed solutions

Singh, Virendra; Yemez, I.; Sotomayor, Jorge

doi:10.1190/tle32111340.1

Cited by 11 publications

(6 citation statements)

References 10 publications

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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%

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%

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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“…The distribution of reservoir properties such as porosity and permeability is a direct function of a complex combination of sedimentary, geochemical, and mechanical processes (Skalinski and Kenter, 2014). The impact of reservoir petrophysics on well planning and production strategies makes it imperative to use reservoir modelling techniques that present realistic property variations via 3-D models (Deutsch and Journel, 1999;Caers and Zhang, 2004;Hu and Chugunova, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…However, the absence of detailed three-dimensional depositional frameworks to guide property modelling inhibits the use of stratigraphic patterns to capture subsurface property variations (Burges et al, 2008). Reservoir modelling techniques with the capacity to integrate forward stratigraphic simulation outputs with stochastic modelling techniques for subsurface property modelling will improve reservoir heterogeneity characterization, because they more accurately produce geological realism than the other modelling methods (Singh et al, 2013). The use of geostatistical-based methods to represent spatial variability of reservoir properties has been in many exploration and production projects (Kelkar and Perez, 2002).…”

Section: Introductionmentioning

confidence: 99%

Porosity and permeability prediction through forward stratigraphic simulations using GPM™ and Petrel™: application in shallow marine depositional settings

Otoo

Hodgetts

2021

Geosci. Model Dev.

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Abstract. The forward stratigraphic simulation approach is applied to predict porosity and permeability distribution. Synthetic well logs from the forward stratigraphic model served as secondary data to control porosity and permeability representation in the reservoir model. Building a reservoir model that fits data at different locations comes with high levels of uncertainty. Therefore, it is critical to generate an appropriate stratigraphic framework to guide lithofacies and associated porosity–permeability simulation. The workflow adopted for this task consists of three parts: first, there is simulation of 20 scenarios of sediment transportation and deposition using the geological process modelling (GPM™) software developed by Schlumberger. Secondly, there is an estimation of the extent and proportion of lithofacies units in the stratigraphic model using the property calculator tool in Petrel™. Finally, porosity and permeability values are assigned to corresponding lithofacies units in the forward stratigraphic model to produce a forward stratigraphic-based porosity and permeability model. Results show a forward stratigraphic-based lithofacies model, which depends on sediment diffusion rate, sea-level variation, sediment movement, wave processes, and tectonic events. This observation is consistent with the natural occurrence, where variations in sea level, sediment supply, and accommodation control stratigraphic sequences and therefore facies distribution in a geological basin. Validation wells VP1 and VP2 showed a notable match after a comparing the original and forward stratigraphic-based porosity models. However, a significant discrepancy is recorded in the permeability estimates. These results suggest that the forward stratigraphic modelling approach can be a practical addition to geostatistical-based workflows for realistic prediction of porosity and permeability.

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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%

“…Improved parallel networking algorithms have significantly decreased the Central Processing Unit (CPU) run time. These advances have led to an exponential increase in the number of cells of the 3D reservoir models from a few thousands cells in 1990's to billions of cells in recent years and a significant decrease in their cell size from 300 -600m in 1990's to 5 -10m in recentyears (Singh et al, 2013). The reduced CPU run time for dynamic simulation has significantly reduced or eliminated up-scaling of large size 3D static reservoir models.…”

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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Integrated 3D reservoir interpretation and modeling: Lessons learned and proposed solutions

Cited by 11 publications

References 10 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

Porosity and permeability prediction through forward stratigraphic simulations using GPM™ and Petrel™: application in shallow marine depositional settings

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

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