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Record W2808982224 · doi:10.2118/191153-ms

Modelling of Fluid Flow in a Petroleum Reservoir Using an Engineering Approach

2018· article· en· W2808982224 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Trinidad and Tobago Section Energy Resources Conference · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPartial differential equationDiscretizationReservoir simulationComputer scienceFluid dynamicsAlgebraic equationFlow (mathematics)Applied mathematicsDifferential equationMathematical optimizationMathematicsNonlinear systemMathematical analysisEngineeringMechanicsPhysicsGeometry

Abstract

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Abstract The mathematical approach is the most commonly used approach in reservoir simulation. The classical mathematical approach considers numerous impractical assumptions leading toward the development of unrealistic reservoir simulator. In contrast, recently developed engineering approach is much promising as it has numerous advantages, such as – scope of bypassing the formulation of partial differential equations and discretization of partial differential equations, the ability to avoid rigorous and complex mathematics, and capability of realistic representation of reservoir behaviour through eliminating spurious assumptions. The present study outlines the route map for developing a reservoir simulator using an engineering approach. Major challenges encountered in reservoir simulation and the fundamentals of various available modelling approaches are addressed in this paper. The outlook for both classical mathematical approach and engineering approach are reviewed along with their strengths and weaknesses. Fluid flow equations are derived based on the proposed engineering approach. To do that, a set of non-linear algebraic flow equations in the time integral form is developed using the mass balance equation, an equation of state, and a constitutive equation without going through the formulation of partial differential equations and discretization step. The time integral is then approximated to obtain the non-linear algebraic flow equations for all the gridblocks of the reservoir. The significance of the engineering approach for describing the accurate fluid flow through porous media is compared to the to conventional mathematical approach. The engineering approach provides the same fluid flow equations as the classical mathematical approach for both the radial cylindrical and cartesian coordinate system but, without going through the formulation of partial differential equations and discretization step. Much simpler ordinary differential equation solvers, e.g., Runge-Kutta method or Euler method can be used in the engineering approach to obtain the solution, whereas the classical mathematical approach does not have this advantage. Both the classical mathematical approach and the engineering approach treat the initial conditions in the same way. If classical mathematical approach uses second-order approximation then the same accuracy is obtained for both approaches in treating the boundary conditions. The engineering approach provides more precise dealing to the constant pressure boundary condition for block-centred gridding system in case of using the first-order approximation. The engineering approach gives the justification of using the central difference approximation for second order space derivative in classical mathematical approach. Results show that the proposed engineering approach based fluid flow model provides better flow prediction than the conventional mathematical approach based flow model. The outcome of this study will help engineers and researchers to develop more transparent simulator instead of creating a black box where the natural chaotic behaviour of the underground reservoir will be more understandable.

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.278
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.266
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it