Practical Applications of Water Hammer Analysis from Hydraulic Fracturing Treatments
Bibliographic record
Abstract
Abstract Water hammer is oscillatory pressure behavior in a wellbore resulting from the inertial effect of flowing fluid being subjected to an abrupt change in velocity. It is commonly observed at the end of large-scale hydraulic fracturing treatments after fluid injection rate is rapidly reduced or terminated. In this paper, factors affecting treatment-related water hammer behavior are disclosed, and field studies are introduced correlating water hammer characteristics to fracture intensity and well productivity. A simulator based on fundamental fluid-mechanics concepts was developed to model water hammer responses for various wellbore configurations and treatment characteristics. Insight from the modeling work was used to develop an optimal process of terminating fluid injection to obtain a consistent, identifiable oscillatory response for evaluating water hammer periodicity, decay rate, and oscillatory patterns. A completion database was engaged in a semi-automated process to evaluate numerous treatments. A data screening method was developed and implemented for enhancing interpretation reliability. Derived water hammer components were correlated to fracture intensity, well productivity and in certain cases, loss of treatment confinement to the intended treatment interval. Using the above process, thousands of hydraulic fracturing treatments were evaluated, and the results of that work are included in this study. The treatments were performed in wells based in Texas, South America, and Canada and completed in low permeability and unconventional reservoirs. The water hammer decay rate was determined to be a reliable indication of the system friction (friction in the wellbore and hydraulic fracture network) that drains energy from the water hammer pulse. In unconventional reservoirs characterized by small differences in the minimum and maximum horizontal stresses, high system friction correlated positively with fracture intensity/complexity and well performance. Results were constrained with instantaneous shut-in pressure (ISIP) and pressure falloff measurements to identify instances of direct communication with previously treated offset wellbores. The resulting analyses provided: – identification of enhanced-permeability intervals – indications of hydraulic fracture geometry – assessment of treatment modifications intended to enhance fracture complexity – identification of loss of treatment confinement to the intended interval – location of associated points of failure in the wellbore Topics covered in the paper include: Introduction Joukowsky Equation Period and Boundary Conditions Review of Prior Work on Water Hammer Analysis Shut-In Pressure Data, Analysis, and Model Data collection frequency Data issues and requirements Water Hammer Analytical Method Water Hammer Model Effects on Water hammer signature Fluid properties Step-down rate change and duration Perforation friction Applications Identification of Boundary Condition Identification of Treatment Stage Isolation Identification of Casing Failure Depth Identification of Excess Period (Excess Length) Case Study – Water Hammer Data in an Unconventional Reservoir Interpretation of frac geometry and friction in the fracture Relationship to well productivity
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".