Estimation of Rock Compressive Strength Using Downhole Weight-on-Bit and Drilling Models
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Bibliographic record
Abstract
Abstract In unconventional gas and tight oil plays, knowledge of the in situ rock mechanical profiles of the reservoir interval is critical in planning horizontal well trajectories and landing zones, placement of perforation clusters along the lateral, and optimal hydraulic fracture stimulation design. In current practice, vertical pilot holes and/or the laterals are logged after drilling, and the sonic and neutron log results are interpreted along with mechanical rock properties measured in the laboratory on core material. However, coring, logging, and core analyses are expensive and time consuming. In addition, as they are typically only performed in a few wells that are assumed to be representative, there is considerable uncertainty in extrapolating results across wide areas with known variability in stratigraphy, faults, thicknesses, hydrocarbon saturations, etc. This paper reports a method for estimating mechanical rock properties and in situ rock mechanical profiles in every well in a development, based on calibration from initial rock core analyses plus drilling data that is routinely acquired. Wellbore friction analysis was coupled with a torque and drag model to estimate in situ unconfined compressive strength (UCS) and Young's modulus (YM) profiles. The key process steps include: Calculate the weight and wellbore friction force of each element of the drill string from bottom to the surface;Adjust the hook load (HL) by subtracting the weight of the hook and entire drill string;Iteratively compute the friction coefficient to match calculated and observed HL;Estimate downhole weight-on-bit (DWOB) by applying a stand pipe pressure correction to the calculated HL and considering potential sliding and abrasiveness;Use a rate of penetration (ROP) model developed for polycrystalline diamond compact (PDC) drill bits considering a force balance between a drill bit geometry and formation and a wear function depending upon the formation abrasiveness and bit hydraulics to compute confined compressive strength (CCS). The resulting CCS was correlated to UCS and YM using regression constants obtained from laboratory triaxial test data on whole core. Using examples from horizontal wells in a siltstone play in Alberta, Canada, this manuscript demonstrates a workflow to estimate rock strength from drilling data. The predicted UCS and YM values were compared with log data and potential uncertainties arising out of drilling data are discussed. Introduction In conventional and unconventional plays alike, a typical way to characterize the subsurface is to make measurements of the formation penetrated by the wellbore with logging tools that are either carried behind the drill bit (logging while drilling) or else run in the well after the drill string is removed (wireline or drill pipe-conveyed logging). Because this adds cost and risk, for unconventional gas or tight oil (UGTO) projects that may have hundreds to even thousands of producers, typically only early appraisal wells plus later, areally scattered wells are designed with extensive logging and laboratory core characterization programs. The assumption is that lateral variability and local heterogeneties are not great and that these data-rich penetrations sufficiently constrain the reservoir properties in the areas between them. In UGTO projects, good representations of the in situ stress profile and geomechanical rock properties are required to optimize the well trajectories and landing zones, placement of perforation clusters along the lateral, and hydraulic fracture stimulation design.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it