Microseismic-Derived Ultimate Expected Stage by Stage Stimulated Reservoir Volume in Unconventional Multi-Fractured Horizontal 10 Well Half Pad - Canadian Horn River Basin Case Study
Bibliographic record
Abstract
Abstract Over the past decade, microseismic monitoring has become the approach most often used to gain an in-situ understanding of the rock's response during hydraulic fracture stimulations. Recently, the utilization of distributed geophone arrays around treatments has provided for an opportunity to investigate the way these fractures develop by examining the microseismic events recorded during a stimulation. Thru microseismic (MS) monitoring currently being carried out in the Horn River formation, geophysicists and reservoir engineers incorporated MS field data and its original interpretation to constrain and validate reservoir models. Generally, we have observed that overall fracture height, width and length, orientation, and growth vary from formation to formation and within each formation. In the presence of uncertainty in reservoir models input data required for determining the best estimate of a value, probabilistic methods are used. Risk analysis is a technique to quantify the impact of uncertainties on output variables, and to determine a range of possible outcomes, as opposed to a single deterministic solution. The uncertainty in the output also provides a measure of the validity of a reservoir model. In this paper we use Monte Carlo simulation using Latin Hypercube sampling by using probability density functions (PDFs) and cumulative distribution functions (CDFs) that describe the likely values of MS fracture half-length and MS fracture height as input parameters into the reservoir model. PDFs and CDFs can be established and used to provide more realistic estimates of stimulation parameters such as Stimulated Reservoir Volume (SRV) which are calibrated with MS SRVs Microseismic Interpretation of events provides an upper bound value for stage fracture half-length and fracture height in unconventional shale gas reservoirs. The cumulative distribution function of stage fracture half-length and fracture height on a well basis on a multi-fractured horizontal well pad provides insight on the understanding of what the ultimate stage by stage SRV could be after pressure depletion occurs and what are the implications for well spacing and well placement in multiwell pad design. This allows a probabilistic approach to production forecasting and reserves estimation and to calculate a robust P10 estimate of Expected Ultimate Recovery and Recovery Factor. This approach differs from previous work that is based on strong collaborative work between geophysicists and reservoir engineers. This paper will show a more recent multi-well pad and, microseismic interpretation, and contribute technically to the knowledge of the unconventional shale oil and gas industry through the Integration of two multidisciplinary disciplines such as Geophysics and Engineering, constrain of Analytical Models which can be used as quick look first pass into numerical simulation deck files, and estimation of production forecasting and different reserves categories including Proved Developed Producing (PDP), Proved + Probable Developed Producing (PPDP) and Undeveloped (PUD & PPUD). Furthermore, some of the design considerations of the newly completed multi-well pad, surveillance data such as production logs, chemical fluid and radioactive tracers and production indications will be addressed and considered, concluding with a discussion of the results.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".