A Simple Method for Quantifying Inter-Well Communication Using Production Data from Single-Phase Shale Gas Reservoirs
Bibliographic record
Abstract
Abstract Hydraulic fracture/reservoir properties and fluid-in-place can be quantified by using rate-transient analysis (RTA) techniques applied to flow rates/pressures gathered from multi-fractured horizontal wells (MFHWs) completed in unconventional reservoirs. These methods are commonly developed for the analysis of production data from single wells without considering communication with nearby wells. However, in practice, wells drilled from the same pad can be in strong hydraulic communication with each other. This study aims to develop the theoretical basis for analyzing production data from communicating MFHWs completed in single-phase shale gas reservoirs. A simple and practical semi-analytical method is developed to quantify the communication between wells drilled from the same pad by analyzing online production data from the individual wells. This method is based on the communicating tanks model and employs the concepts of macroscopic material balance and the succession of pseudo-steady states. A set of nonlinear ordinary differential equations (ODEs) are generated and solved simultaneously using the efficient Adams-Bashforth-Moulton algorithm. The accuracy of the solutions is verified against robust numerical simulation. In the first example provided, a MFHW well-pair is presented where the wells are communicating through primary hydraulic fractures with different communication strengths. In the subsequent examples, the method is extended to consider production data from a three-well and a six-well pad with wine-rack-style completions. The developed model is flexible enough to account for asynchronous wells that are producing from distinct reservoir blocks with different fracture/rock properties. For all the studied cases, the semi-analytical method closely reproduces the results of fully numerical simulation. The results demonstrate that, in some cases, when new wells start to produce, the production rates of existing wells can drop significantly. The amount of productivity loss is a direct function of the communication strengths between the wells. The new method can accurately quantify the communication strength between wells through transmissibility multipliers between the hydraulic fractures that are adjusted to match individual well production data. In this study, a new simple and efficient semi-analytical method is presented that can be used to analyze online production data from multiple wells drilled from a pad simultaneously with minimal computation time. The main advantage of the developed method is its scalability, where additional wells can be added to the system very easily.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".