Formation-Fluid Microsampling While Drilling Enables Complete Reservoir Characterization
Bibliographic record
Abstract
This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 195806, “Formation-Fluid Microsampling While Drilling: A New PVT and Geomechanical Formation-Evaluation Technique,” by Julia Golovko, Christopher Jones, SPE, and Bin Dai, Halliburton, et al., prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September-2 October. The paper has not been peer reviewed. Pressure/volume/temperature (PVT) phase behavior characterization and geochemical compositional analysis of petroleum samples play a crucial role in the determination of producible reserves and the best production strategy. Openhole samples are the most-valuable types of samples for PVT and geochemical analysis but are costly and limited to 10 to 20 samples. The complete paper presents a technical discussion of a new microsampling technique for logging while drilling (LWD) and a corresponding wellsite technique to provide compositional interpretation, contamination assessment, reservoir-fluid compositional grading, and reservoir compartmentalization assessment. This microscale approach enables fast analysis by using field or near-field deployment of the analytical tool. The results inform planning for wireline sample retrieval, if necessary. Technique Overview The microsampler used in the downhole tool can collect reservoir fluid in small quantities suitable for compositional analysis. Because of its small size, the microsampler can gather multiple fluids at various reservoir depths, while PVT sampling requires larger volumes and has more constraints. However, when used in combination with conventional PVT-grade samples, the microsamples can provide significant chemical profiling. The 40-ml quantity provides the ability to collect many more samples than the conventional PVT sample size of 200 to 1,000 ml. Additionally, 40 ml provides more than enough of a sample for a complete chemical analysis using a liquid chromatograph or gas chromatograph coupled with either a mass spectrometer for biomarker analysis or a flame-ionization detector (FID) for a complete assay. Isotope analysis is also possible. Recovery to surface of fluid samples collected at reservoir temperature and pressure allows for analysis with an automated gas chromatograph (GC) deployed in the field, providing reduced labor and rapid analysis. The unique injection chamber of the GC is designed with the injection port and valve configured to withstand pressure up to 5,000 psi, a tolerance approximately five times higher than that of standard GC injection valves. This allows for injection of the microsample with a solvent carrier as a single-phase fluid so that analysis can provide composition and fluid properties such as gas/oil ratio without a flash. The GC has two detectors, including an FID for hydrocarbon components and a thermal conductivity detector for inorganic gas components such as carbon dioxide, nitrogen, and hydrogen sulfide. The system can quantify hydrocarbon components from C1 to C36 and perform contamination studies of oil samples with drilling fluids. According to the authors, the technique enables reservoir engineers to characterize a reservoir completely without limit to the number of acquired samples. They write that, in combination with conventional PVT samples, it is possible to extrapolate PVT properties to all pump-out stations and conduct a complete geochemical profile of the reservoir.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".