Formation-Fluid Microsampling While Drilling Enables Complete Reservoir Characterization
Notice bibliographique
Résumé
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 The paper has not been peer reviewed.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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».