A New Straight-Line Analysis Method for Estimating Fracture/Reservoir Properties Using Dynamic Fluid-in-Place Calculations
Bibliographic record
Abstract
Summary Straight-line analysis (SLA) methods, which are a subgroup of model-based techniques used for rate-transient analysis (RTA), have proved to be immensely useful for evaluating unconventional reservoirs. Transient data can be analyzed using SLA methods to extract reservoir/hydraulic-fracture information, whereas boundary-dominated-flow (BDF) data can be interpreted for fluid-in-place estimates. Because transient-flow periods might be extensive, it is also advantageous to evaluate the volume of hydrocarbons in place contacted over time to assist with reserves assessment. The new SLA method introduced herein enables reservoir/fracture properties and contacted fluid in place (CFIP) to be estimated from the same plot, which is an advantage over traditional SLA techniques. The new SLA method uses the Agarwal (2010) approach for CFIP estimation, extended to variable-rate/pressure data for low-permeability (unconventional) reservoirs. A log-log plot of CFIP vs. material-balance time (for liquids) or material-balance pseudotime (for gas) is created, which typically exhibits power-law behavior during transient flow, and reaches a constant value [original fluid in place (OFIP)] during BDF. Although CFIP calculations do not assume a flow geometry, the SLA method requires this to extract reservoir/fracture information. Herein, transient linear flow (TLF) is assumed and used for the SLA-method derivation, which allows the linear-flow parameter (LFP) to be extracted from the y-intercept (at material-balance time or material-balance pseudotime = 1 day) of a straight-line fit through transient data. OFIP can also be obtained from the stabilization level of the CFIP plot. Validation of the new SLA method for an undersaturated oil case is performed through application to synthetic data generated with an analytical model. The new SLA results in estimates of LFP and OFIP that are in excellent agreement with model input (within 2%). Further, the results are consistent with the traditional SLA methods used to estimate the LFP (e.g., the square-root-of-time plot) and the OFIP (e.g., the flowing material-balance plot). Practical application of the new SLA method is demonstrated using field cases and experimental data. Field cases studied include online oil production from a multifractured horizontal well (MFHW) completed in a tight oil reservoir, and flowback water production from a second MFHW, also completed in a tight oil reservoir. Experimental (gas) data generated using a recently introduced RTA core-analysis technique were also analyzed using the new SLA method. In all cases, the new SLA-method results are in excellent agreement with traditional SLA methods. The new SLA method introduced herein is an easy to apply, fully analytical RTA technique that can be used for both reservoir/fracture characterization and hydrocarbon-in-place assessment. This method should provide important, complementary information to traditionally used methods, such as square-root-of-time and flowing material-balance plots, which are commonly used by reservoir engineers for evaluating unconventional reservoirs. The method is currently limited to cases exhibiting single-phase flow, the flow-regime sequence of TLF to BDF, and reservoir homogeneity. In future work, these limitations will be resolved.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".