Fracture Driven Interaction (FDI) Diagnostics and Modeling at a Shut-In Open-Hole-Multi-Lateral Producing Well
Bibliographic record
Abstract
Abstract During horizontal fracturing operations adjacent shut-in producing wells are likely to experience multiple Fracture Driven Interactions (FDI's), as production and pressure communication may occur along the entire horizontal section. These contrast with more commonly used Plug and Perf (P&P) zipper fracturing operations on new well pads, where only a single observation well stage is in pressure communication with the reservoir at a time. As a result, FDI's may be infrequent on a pad of exclusively new wells. With an offsetting producing observation well, natural gas and oil fluid columns may be present in the vertical section of the wellbore. This significantly changes the FDI pressure response compared to the case where the wellbore is completely full of water. A FDI analysis modeling technique has been developed when the observation wellbore has a gas, oil and water column. The only data requirements are routine pressure and rate data at the treatment well plus surface pressure and fluid levels (or alternatively BHP) at the temporarily-shut-in observation well. No observation well conditioning is required. For the observation well, both the FDI pressure values and shape are strongly affected by compressible wellbore fluids and the conductivity of the static fracture between the observation well and the dynamic fracture growing from the treating well. This project shows the value of combining surface pressure gauge data with fluid level shots to pre-calculate the wellbore storage coefficient and the cumulative fluid entering the observation well as a function of time. Surface pressure combined with fluid level data is therefore superior to bottom hole pressure data alone. Knowledge of cumulative fluid influx into the wellbore puts additional constraints on FDI modeling work. The shut-in observation well pressure response is analogous to a closed-chamber test. The closed-chamber model includes the effects of wellbore storage, skin, and the possible pre-existing finite conductivity static fracture (black line in Figure 1). The treatment well's dynamic fracture model calculates the pressure at the contact point (or FDI Point) depending upon whether a hydraulic or poroelastic (stress shadow) model is used, as was shown in Nicholson et al. (2021). The closed chamber model then calculates the pressure at the measurement point (BHPFDI), the rate and cumulative influx at Point A of Figure 1a and 1b. An application of this new technique is presented for an Alberta East-Central Mannville oil play. A new Multi-Fractured-Horizontal (MFHZ) well produced FDI's at a shut-in Open-Hole-Multi-Lateral (OHML) well. The workflow included: A Diagnostic Fracture Injection Test (DFIT) performed to measure closure pressure and Far-field Fracture Extension Pressure (FFEP) in the treating well. Frac-Stage Pressure Fall-off Analyses for comparison of net pressure measurements for full-scale fracturing stage rates, volumes and fluid viscosity to the DFIT. Fracture Driven Interaction (FDI) modeling with the closed chamber add-on for determining the hydraulic fracture dimensions including length and height. With a modest amount of planning and data collection oversight, observation well pressure and fluid levels can provide fracture height, length and perforation cluster efficiency (where applicable) comparable to higher cost diagnostic technologies such as microseismic monitoring, fibre-optics, or down hole imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".