An Integrated Completion and Reservoir Modeling Methodology for Horizontal Shale Wells: A Montney Formation Example
Bibliographic record
Abstract
Abstract The Montney Formation is one of the largest unconventional resources in North America covering from southwestern Alberta to northeast British Columbia. The Montney Formation has natural gas, natural gas liquids (NGL) and oil from conventional and unconventional reservoirs. The first multiple fractured horizontal well (MFHW) was drilled in 2005. However, since the first MFHW, different methods were proposed to optimize completion designs in the Montney Formation. Some of the optimized completions utilized the "operational effectiveness" of high-rate slick-water fracture designs while other designs utilized energized fracturing fluids. What has been missing was an integrated methodology that utilizes all available data to improve well stimulation and productivity. The objective of this paper is to present a new methodology for selecting lateral well placement, completion strategy and determination of stimulated reservoir volume (SRV) by integrating available data such as curvature data from 3D seismic, micro-seismic, geo-mechanical data, logs, fracability index, mini-frac test, step-rate test, DFIT analysis, core data, and fracture treatment design to optimize well productivity, hydrocarbon recovery and economics. The process of developing the hybrid model involved integrating the completions design with compositional reservoir simulator using a two-step process; first, the hydraulic fracture design was calibrated using only the micro-seismic data from the stages that were closest to the geophone/receiver (avoiding location bias or signal-noise ratio issues) in order to develop a reliable fracture geometry model. The calibrated fracture model was used for history matching and re-modeling of all the fracture stages in each well. Fracture geometry and dimensions for each stage were obtained from the calibrated fracture model. Secondly, the compositional reservoir simulators were built using reservoir geology, PVT data, production data and well deviations. Fracture dimensions obtained from the calibrated fracture model were then transferred into the reservoir simulator. Finally, curvature data obtained from 3D seismic was used to predict the location of secondary fissures within the well drainage area, and were then incorporated into the compositional reservoir simulator. The result from this study shows that the hybrid integrated completion and reservoir model can be used for the selection of optimum lateral placement targeting sweet spots that have secondary fissures and good fracability index to maximize production rates, hydrocarbon recovery and to improve well economics. Additionally, this study presents a new hybrid model for determining a representative stimulated reservoir volume (SRV) with discrete fracture networks that captures secondary fissures, which can then be used for production history matching and forecasting. The key features of this work that will benefit the petroleum industry are:A new methodology for building calibrated fracture model using micro-seismic survey even if the micro-seismic data is of low quality as a result of location bias or signal-noise ratio issuesExtending the stimulated reservoir volume (SRV) to include secondary fissure contributions to the overall well production and recovery.Use of a discrete fracture network with stress dependent fracture permeability in the compositional reservoir simulator to capture the effects of geomechanical changes during depletion.A comparison of well productivity and EUR derived from a planar fracture model versus discrete fracture network based reservoir models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".