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Record W2090572424 · doi:10.2118/137816-ms

Integrated Reservoir and Decision Modeling to Optimize Spacing in Unconventional Gas Reservoirs

2010· article· en· W2090572424 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Unconventional Resources and International Petroleum Conference · 2010
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsUnconventional oilFlexibility (engineering)Computer scienceProfitability indexInfillMonte Carlo methodReservoir simulationFossil fuelNet present valuePetroleum engineeringDrillingMathematical optimizationProduction (economics)GeologyEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Abstract Despite our increased experience, unconventional gas plays remain risky. In the face of this risk, operators must balance the need to conserve capital and protect the environment by avoiding over drilling with the desire to maximize profitability by achieving the optimal well spacing as quickly as possible. Previous unconventional gas developments such as the Carthage Field (Cotton Valley) have implemented multiple infill drilling programs over several decades to optimize well spacing, with significant reduction in value (McKinney et al. 2002). However, in emerging plays such as the non-core Barnett Shale and the Fayetteville Shale, historical infill programs are not available to evaluate optimal spacing and we do not have the luxury of developing these fields over the next 30-40 years. Existing approaches for optimizing development, such as integrated reservoir simulation studies or statistical moving-window methods, can be either prohibitively time-consuming and expensive or they do not consider the uncertainty inherent in the assessment. The objective of our work was to develop technology and tools to help operators determine optimal well spacing in highly uncertain and risky unconventional gas reservoirs as quickly as possible. To achieve the research objectives, we developed an integrated reservoir and decision modeling system that incorporates uncertainty. We used Monte Carlo simulation with a fast, approximate reservoir simulation model to match and predict production performance in unconventional gas reservoirs. Simulation results are then integrated with a Bayesian decision model that accounts for the risk facing operators. We applied these integrated tools to a hypothetical case based on data from Deep Basin (Gething) tight gas sands in Alberta, Canada, to determine optimal development strategies. We anticipate that the tools and methodologies developed will be applicable in most shale and tight gas reservoirs. These tools should ultimately be able to help operators determine, for example, the combination of primary development strategy (well spacing and/or completion method) and testing (pilot downspacings and/or tests of other completion methods) that maximizes future profitability. The optimal design of such programs in unconventional reservoirs, where the risks are high, is likely to pay large dividends.

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)0.001
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 score0.042
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.260
Teacher spread0.236 · 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