Towards Field Testing of the Flowback Technology for Multistage-Fractured Horizontal Wells: Modeling-Based Design and Practical Implications
Bibliographic record
Abstract
Abstract Selection of an optimum scenario of well flowback and startup after multistage fracturing and the design of the field experiment are based on mathematical modeling of filtration in a propped fracture, taking into account the compaction of a proppant pack, embedment of proppant into the fracture faces, tensile failure of the fracture walls and proppant flowback. For this purpose, we have proposed a simulation-based process for the development of a series of field trials for the well flowback after multistage fracturing. There are two different scenarios of flowback regime (smooth and aggressive) with a step-wise increase in the diameter of the choke to determine the upper and lower limits of the safe operating envelope of the well flowback. Based on the results of a series of parametric calculations, the preferred ranges of implementation of the flowback technology are formulated in terms of the steps of change and the duration of the periods of constant diameter of the choke, which are planned to be tested in the field. Several horizontal wells with hydraulic fracturing at the terrigenous field of Western Siberia will be planned to study the impact of inflow dynamics on the pipeline. To test the research hypotheses, two limiting scenarios of the flowback were proposed in terms of the dynamics of the opening of the choke with time: "smooth" and "aggressive" (the well is flowing without assistance, before the installation of the ESP). In addition to monitoring the parameters of well production (flow rate, water cut, bottom hole and wellhead pressure), suspension samples will be taken to analyze the particulate content, according to a pre-approved schedule for the purpose of granulometric analysis of the solid phase. The results of parametric numerical calculations, based on our in-house hydrodynamic and geomechanical models, allowed us to determine the critical values of drawdown, in which undesirable geomechanical phenomena are realized (primarily proppant flowback, proppant embedment into the fracture walls and compression of the proppant pack under the action of closure stresses and associated reduction in the width of the propped fracture, as well as the risk of rock failure). Parametric model-based studies suggest a hypothesis for testing in the field that the accumulated production after hydraulic fracturing depends on the dynamics of flowback and well startup. To select candidate wells, we use the results of solving the inverse problem to determine the governing parameters of the formation, in which undesirable geomechanical effects are most pronounced, as well as to take into account the requirements for the representativeness and repeatability of field tests. On the basis of the field data, a decision tree will be formed to maximize the efficiency of the experiment and operational supervision. The authors are aware of the scale of uncertainties associated with the interpretation of data obtained as a result of field experiments. The demand from the operator to improve the methodology of hydraulic fracturing in the region allows us to analyze the current methods of conducting flowback operations. Conducting field tests is possible without significant capital expenditures, since it does not require additional equipment on test wells. We proposed a simulation-based process for the development of a series of field trials on flowback.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".