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Record W2076404661 · doi:10.2118/171593-ms

History-Matching and Forecasting Tight/Shale Gas Condensate Wells Using Combined Analytical, Semi-Analytical, and Empirical Methods

2014· article· en· W2076404661 on OpenAlexaff
Christopher R. Clarkson, J. D. Williams-Kovacs, Farhad Qanbari, Hamid Behmanesh, M. Heidari Sureshjani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWorkflowPetroleum engineeringTight gasComputer scienceShale gasRevenueEmpirical researchCurrent (fluid)Matching (statistics)Oil shaleOperations researchGeologyHydraulic fracturingEngineeringMathematicsEconomics

Abstract

fetched live from OpenAlex

Abstract The primary focus of the majority of current, and foreseeable, natural gas drilling within North America is low-permeability liquid-rich gas and gas condensate reservoirs, where the liquid fraction is now a major source of revenue. Development of these liquid-rich resources is aided by the use of multi-fractured horizontal wells (MFHWs), and is at an early stage; further research is required to appropriately manage the resource for optimal hydrocarbon recovery. The appropriate forecasting methodologies to apply to these tight liquid-rich plays are a focus of current research. Although numerical simulation, which can account for complex PVT, reservoir and fracture characteristics in these liquid-rich plays, is the most rigorous method for forecasting, this technique usually cannot be applied to every well in a field because of a lack of supporting data and time required for analysis. Empirical methods provide an alternative for routine forecasting, but their lack of a physical basis means that model fitting parameters are difficult to constrain, leading to large uncertainties in forecasting. Analytical methods, while capable of incorporating more rigorous physics, require more information than empirical methods and likely also cannot be applied to every well in a field. In order to address the limitations of existing empirical and analytical methods for forecasting MFHWs producing from liquid-rich tight gas/shale, we demonstrate application of a workflow recently introduced by Clarkson (2013b). In this workflow, analytical models are first used to history-match and forecast MFHWs that have sufficient data, and then empirical models are used to match the analytical model forecast to constrain model parameters for wells in which the analytical methods cannot be applied. For this purpose, a suite of analytical models are proposed, that can model a range in flow-regime sequences from simple linear-to-boundary flow scenarios, to more complex flow regime sequences exhibited by MFHWs with branched fractures. Similarly, a suite of empirical methods are used, and the models yielding the most accurate matches to the analytical models are selected for forecasting. Lastly, in order to bridge the gap between analytical and empirical methods, we utilize the recently developed semi-analytical method introduced by Clarkson and Qanbari (2014), which has as its basis the contacted gas-in-place calculations of Agarwal (2010). Although the analytical and semi-analytical models used in this work are strictly applicable to single-phase flow scenarios, we have demonstrated using simulation cases (as have others) that constant condensate gas ratios can occur for tight/shale gas condensate wells exhibiting transient linear flow and flowing at near constant flowing bottomhole pressure. For these cases, the single-phase forecasting methods can be applied, and both gas and condensate phases may be forecast accurately, even if multi-phase flow is occurring in the reservoir. We demonstrate the accuracy of these methods using simulated cases, and apply our workflow to an actual field example of a liquid-rich shale MFHW. This study will be of interest to those petroleum engineers who are faced with forecasting a large number of liquid-rich shale wells, and desire methods that can be simply applied to constrain forecasts and improve accuracy.

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.

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: Empirical
Teacher disagreement score0.167
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.059
GPT teacher head0.312
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

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

Citations23
Published2014
Admission routes1
Has abstractyes

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