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Record W2056023904 · doi:10.2118/161350-ms

A New Simulation-Based Process to Predict the Impact of Hydraulic Fracture Parameters on EUR: A Tight Gas Field Example

2012· article· en· W2056023904 on OpenAlex
Can Yetkin, Tuba Firincioglu, A. M. Haney

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 Eastern Regional Meeting · 2012
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHydraulic fracturingFracture (geology)Reservoir simulationPetroleum engineeringMonte Carlo methodPermeability (electromagnetism)Tight gasGeologyHydraulic conductivityPorosityMechanicsGeotechnical engineeringMathematicsSoil science

Abstract

fetched live from OpenAlex

Abstract This study develops a process that determines the critical hydraulic fracture parameters and quantifies their impact on the EUR by combining reservoir simulation with probabilistic analysis methods. The process is verified by a real field case example in a tight gas reservoir. The final product can be applied to other unconventional reservoirs to ultimately maximize revenues by planning superior fracturing operations and optimizing well spacing. A detailed dual porosity, 1 section reservoir model was created and history matched to model the flow mechanism. A fine layered (2-3ft) geostatistical model was utilized in simulation without upscaling. The dual porosity formulation enabled the simulation model to represent the hydraulic fracture - matrix interaction properly so that the flowback and formation water production could be matched also. During the history matching phase, the parameters that control the impact of hydraulic fractures on the recovery were identified as follows:Matrix-fracture exchange: this parameter represents the complexity of the fractures and is controlled by the surface area created during hydraulic fracturing.Fracture conductivity: this is effectively the permeability of the hydraulic fractureHalf length: this parameter impacts the extent of the hydraulic fracture, therefore the amount of matrix that has been accessed.Job size: The size of the frac fluid volume injected during the hydraulic fracturing process In this work, an internal proprietary technology that creates a response surface for the combination of the parameters defined above was utilized. This technology utilizes Experimental Design, Response Surfaces and Constrained Monte Carlo. The history matched simulation model was automatically modified to create the necessary cases to calculate a multi-dimensional response surface. The created response surface was then used to do Monte Carlo simulations to create P10 to P90 probabilities of the total gas production (EUR). The results of the study allowed us to understand not only the mechanisms operating in the reservoir being studied, but also the required hydraulic fracture parameters (ranges) to achieve a given EUR of a specific probability. The same algorithms were then be used to predict the future performance of other well spacing patterns and hydraulic fracture job sizes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.579

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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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