Integration of Microseismic and Other Post-Fracture Surveillance With Production Analysis: A Tight Gas Study
Bibliographic record
Abstract
Abstract Quantitative production analysis of tight gas reservoirs has historically been a challenge due to complex reservoir characteristics (ex. lateral and vertical heterogeneity, stress-sensitivity of permeability and porosity), induced hydraulic fracture properties in vertical wells (ex. multi-phase flow, conductivity changes, complex fracture geometries), operational complexities (ex. variable back-pressure, liquid-loading) and data quality (infrequent rate or flowing pressure reporting). All of these challenges conspire to make extraction of reservoir (kh and OGIP) and hydraulic fracture properties (xf and fracture conductivity) soley from production/flowing pressure data difficult, often resulting in non-unique answers. In recent history, there has been the added complication that tight gas (and most recently shale gas) reservoirs are now being exploited with horizontal wells, often stimulated using multiple hydraulic fracture stages, which imparts greater complexity on the analysis. Flow regime identification, which is critical to the correct analysis, is more complicated than ever owing to the number of possible flow regimes encountered in such wells. A case study is presented in which it is demonstrated that modern post-fracture surveillance data, such as microseismic and post-frac production logging, aids in both model identification and model calibration, which is critical to the analysis of hydraulically-fractured horizontal wells completed in tight gas formations. A workflow is presented in which offset vertical wells (to the horizontal wells) are first analyzed to obtain estimates of kh and hydraulic fracture properties, followed by commingled-stage and single-stage production analysis of the multi- (transverse) hydraulic fracture horizontal wells. Microseismic data is incorporated into the analysis of the horizontal wells to 1) understand the orientation and degree of complexity of the induced hydraulic fractures and 2) constrain interpretations of effective hydraulic fracture lengths from production data analysis. It is also demonstrated that once the commingled stage analysis of the horizontal wells is completed, the total interpreted effective hydraulic fracture half-length may be allocated amongst the stages using a combination of production logs and tracer logs. The primary contribution of the current work is the presentation of workflows, emphasizing the integration of various data sources, to improve production analysis of multi-frac’d horizontal wells completed in tight gas formations. In addition to the workflows, it is shown that a combination of advanced production analysis approaches, including methods analogous to classic pressure transient analysis, production type-curve matching and simulation, may be necessary to arrive at a unique analysis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".