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Record W2973046315 · doi:10.2118/191459-pa

Physics-Based Fluid-Flow Modeling of Liquids-Rich Shale Reservoirs Using a 3D Three-Phase Multiporosity Numerical-Simulation Model

2019· article· en· W2973046315 on OpenAlexafffundabout
Bruno A. Lopez Jimenez, Roberto Aguilera

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersOntario Ministry of Food and AgricultureChina National Offshore Oil CorporationUniversity of Calgary
KeywordsKerogenOil shaleKnudsen diffusionPorosityPetroleum engineeringFluid dynamicsHydraulic fracturingReservoir simulationDesorptionGeologyDiffusionFlow (mathematics)AdsorptionMechanicsGeotechnical engineeringChemistrySource rockThermodynamics

Abstract

fetched live from OpenAlex

Summary Production from liquids–rich shale reservoirs in the US and Canada has increased significantly during the past few years. However, a rigorous understanding of shale rocks and fluid flow through them is still limited and remains a challenge. Thus, the objective of our research is developing a 3D physics–based model for simulating fluid flow through these types of multiporosity rocks. This is important given the recent spread of these types of reservoirs throughout the world. Simulation of liquids–rich shale reservoirs is performed with the construction of an original fully implicit 3D multiphase modified black–oil finite–difference numerical formulation, which uses a multiporosity approach as well as diffusion from solid kerogen. The multiporosity system includes adsorbed porosity, organic porosity, inorganic porosity, natural–fracture porosity, and hydraulic–fracture porosity. A numerical model is developed with capabilities to handle dissolved gas in the solid part of the organic matter, adsorption/desorption from the organic pore walls, viscous– and non–Darcy–flow mechanisms (slip flow and Knudsen diffusion), and stress–dependent properties of natural and hydraulic fractures. Examples of simulated results are presented as crossplots of pressure, production rates, and cumulative production vs. time. These plots are used to show the contributions of free gas, adsorbed gas, and dissolved gas to fluid production from liquids–rich shale reservoirs. Results indicate that both desorption and gas diffusion positively affect shale performance. Simulation results demonstrate that not taking into account desorption and diffusion from solid kerogen leads to underestimating production from liquids–rich shale reservoirs. Furthermore, the simulation study shows that long periods of time are required for the effects of these two mechanisms to be manifested. This helps to explain why shales have been produced over long periods of time (several decades), such as in the case of Devonian wells in the Appalachian Basin. The type of 3D simulation model for multiporosity liquids–rich shale reservoirs developed in this paper is not currently available in the literature. The approach implemented in this paper provides a novel and important foundation for simulating complex shale reservoirs.

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How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.534
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.315
Teacher spread0.253 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2019
Admission routes3
Has abstractyes

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