Permeability Estimation From Inflow Data During Underbalanced Drilling
Bibliographic record
Abstract
Abstract Underbalanced drilling has become increasingly popular as it prevents fluid invasion during drilling operations. Consequently, formation damage may be reduced. This is particularly important in the case of depleted reservoirs or when horizontal and deviated wells are drilled. As a result of the lower pressure in the wellbore there is an inflow from the reservoir into the wellbore, which is continuously measured at the wellhead. This inflow carries information about the reservoir. The objective of this paper is to develop a mathematical model and its associated interpretation methods to estimate reservoir permeability and its variation along the wellbore using the inflow measurements at the wellhead. The traditional methods of pressure and rate transient analysis are not applicable to underbalanced drilling data particularly because the length of the producing interval is continuously increasing with time. In this paper we will develop a mathematical model accounting for this complication. This model calculates inflow rates in the forward mode and the reservoir permeability when used in the backward mode. We have validated our methodology against synthetic data obtained from numerical simulation, and applied it to a number of actual field cases. In field studies, after estimation of the permeability profile along the wellbore, the estimated permeability values were used along with reported bottomhole pressure after end of drilling to calculate gas inflow. This was then compared with the measured total inflow. Good agreement was obtained between the predicted and measured values. Furthermore, a number of sensitivity studies were conducted to examine the sensitivity of the estimated permeability to errors in the reported inflow information and the pressure drop along the wellbore. The results reported in the paper indicate that the estimated permeability profile remained relatively unchanged. Introduction Formation testing during under-balanced (UB) drilling relies on the hypothesis that inflow rate contains enough information from the reservoir to enable determination of some reservoir properties. Acquisition of the flow rate and bottomhole pressure data, and their analysis can offer information about the permeability and its variation along the length of the wellbore. Analyzing the UB drilling data has been the subject of many papers, some of more recent ones are reviewed below. Hunt and Rester[4] modify the standard pressure transient analysis techniques to include time-dependent boundary conditions, which account for the variable well length as the drilling bit progresses in the reservoir. The reservoir parameters are calculated based on a trial and error procedure as part of a history-matching exercise. A more recent paper of the authors[5] extends this to multilayer reservoirs. Kneissl[6] suggests introducing fluctuations to the bottomhole pressure during drilling to calculate both the permeability and the pore pressure during UB drilling. Similarly, Vefring et al[9] show that introducing fluctuations to bottomhole pressure while drilling, can improve the estimation results for calculating both permeability and pore pressure simultaneously. In this paper, as well as their earlier work[8], the authors tie in a dynamic well-flow model with a simple transient reservoir model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".