Integrated Geoscience and Reservoir Simulation Approach to Understanding Fluid Flow in Multi-Well Pad Shale Gas Reservoirs
Bibliographic record
Abstract
Abstract This paper presents a systematic approach to integrate geoscience and dynamic reservoir modeling of two multi-well pads in the Horn River Basin, Canada. The Horn River shale gas play is a world-class unconventional gas resource and is being exploited using multi-stage fracturing along horizontal wells. The two well pads, Pad-1 and Pad-2, selected for this study are comprised of eight and seven wells respectively with 1 to 7 years of production history. Numerical modeling of shale reservoirs has historically been a problematic low-confidence exercise, because of the difficulties associated with inadequate characterization of the geologic framework of shale plays; the problems of estimating the properties of fracture networks; and the complexities of capturing multi-phase flow in fracture networks and wellbores during production, especially in the face of offset wells activity. The work presented in this paper provides useful insights into these issues. The geoscience modeling activity begins with integrating information from cores, well logs, petrophysical analyses and seismic data into a 3D geocellular model. At first, this model was based upon a simple lithostratigraphic concept and this was the basis of the numerical flow modeling exercise of Pad-1. The 3D geocellular model was thereafter thoroughly reworked to incorporate a sequence stratigraphic perspective of the Horn River shale and to include geomechanical considerations based upon the stratigraphic positioning and landing depths of the subject horizontal wells. This reworked geocellular model had a profound impact on the dynamic modeling of the Pad-2. Also hydraulic conductivity of induced and natural fractures was measured on core plugs at reservoir conditions to assign conductivity values to primary, secondary and tertiary flow paths into dynamic reservoir modeling. As a result of the integrated workflow, we have achieved a history match allowing us to further understand the hydraulic fracture behaviour and its impact on producing shale reservoirs within the Horn River Formation. Based on the findings we recommend completion strategy that can produce more than one compartmentalized shale reservoir to optimize production. Ultimately, the objective of any reservoir modelling project is to provide a range of reliable forecast of future performance that is grounded in representative geoscience interpretations and that takes operational constraints into account. The technical learnings described in this work will be helpful to further understand the hydraulic fracturing behaviour and their impact on producing the Horn River shale reservoirs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".