Production-Logging Technologies Enhance Inflow Profiling in Deepwater Gulf of Mexico
Notice bibliographique
Résumé
This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 196188, “Third-Generation Production-Logging Technologies Enhance Inflow Profiling in Deepwater Gulf of Mexico Reservoirs,” by Glenn Donovan, SPE, Sagar Kamath, and Elizabeth Tanis, SPE, Shell, et al., prepared for the 2019 SPE Annual Technology Conference and Exhibition, Calgary, 30 September–2 October. The paper has not been peer reviewed. This paper discusses the effectiveness of third-generation (Gen3) production-logging-tool (PLT) technology, which uses co-located digital sensors for simultaneous acquisition of flow data to provide the most accurate characterization of the flow condition at each depth surveyed. The resulting data allow for much-improved processing. The probabilistic interpretive model used in the processing has been updated to incorporate this and future developments in PLT architecture. Introduction The first generation of PLT provided a single, discrete measurement for each sensor along the tool assembly’s length, resulting in long tool assemblies and measurements taken at different points along the flow path. This approach had several drawbacks: long toolstrings, point sensors that only provided a measurement at a single point in the cross section of the flow, and measurements that were not acquired simultaneously at each depth logged. Second- generation PLTs represented an improvement because sensors were arranged as an array, enabling multiple measurements to be made at a single depth. However, the toolstrings were still long and not all were arranged optimally to capture data in the flow path. The Gen3 PLT is one-tenth the length of the first-generation versions and roughly one-third that of the shortest second-generation tools. Digitalization allows for direct measurement of flow conditions and rapid interpretation of results. In multiphase flow and deviated wells, co-locating sensors in a spatial geometry provides the optimal information with which to create a fully accurate picture of the downhole flow. Description of Gen3 PLT The PLT described in the paper exemplifies how miniaturization and digitalization are enabling transformational improvements over traditional systems in terms of efficiency and capability. The tool encompasses, within a 3-ft length, up to 24 sensors that collect multiple measurements of fluid properties and fluid movements in a wellbore. These include oil, gas, and water holdup and bubble count, fluid conductivity, phase velocities, pressure, temperature, inclination, rotation, and depth correlation, plus power and communication. The fluid characteristics are locally screened by an array of 8 to 16 tube-shaped probe sensors that are interchangeable, depending on the targeted measurements (Fig. 1). The tool relies on a refractive index needle-shaped probe with a triphasic sensor to identify and quantify the oil, gas, and water holdups and bubble counts at each point of the array. The geometric design of the sensitive tip, along with the optoelectronics of the sensor, are optimized to discriminate oil, water, and gas with high confidence, overcoming the fact that the refractive indexes of oil and water are close to each other.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».