New Advances in Production Data Analysis of Hydraulically Fractured Tight Reservoirs
Bibliographic record
Abstract
Abstract Tight reservoirs stimulated by multistage hydraulic fracturing are commonly described by a dual porosity model. This model consists of homogeneous matrix blocks separated by vertical hydraulic fractures. This paper hypothesizes that the production data of some fractured horizontal wells may also be described by a triple porosity model. The third medium can be either reactivated natural fractures or thin horizontal beds with a higher permeability. We test this hypothesis by extending the existing triple porosity models to develop an analytical procedure for determining the reservoir parameters. We derive the simplified equations for different regions of the rate-time plot including linear and bilinear flow regions. These equations can be used to calculate the effective fracture half-length, matrix permeability and length of micro-fractures. We use the proposed model to analyze the production data of two wells drilled in Barnett shale. The results show that a dual porosity model is more appropriate for describing Barnett shale data. Even if the micro-fractures are present they are not inter-connected and the length scale is much smaller than the hydraulic fracture spacing. The second part of this paper focuses on analyzing production data of tight oil reservoirs. We plot rate-normalized pressure (RNP) versus material balance time (MBT) of two wells drilled in Cardium and Bakken formations. We observe a half-slope followed by a unit-slope in both cases. This paper hypothesizes that the half slope reflects the linear transient flow regime, and the unit slope reflects the linear pseudosteady state (PSS) flow. We test this hypothesis by developing a new model for pseudo steady state flow when pressure interference occurs between two adjacent hydraulic fractures. We determine the reservoir properties by both PSS and transient analysis methods. For Cardium well, both methods give very close results. For Bakken well, both methods give relatively close results. We conclude that analyzing the late time production data of tight oil wells complement the linear transient analysis and can be used to calculate the stimulated reservoir volume, which can eventually help the industry to optimize the fracturing operation.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".