Development and Application of Algorithm for Stress Inversion Based on Image Log Data
Notice bibliographique
Résumé
Abstract Successful well planning and stimulation in complex geological settings (especially in the horizontal wells and wells with a high degree of deviation) is bound with conducting geomechanical estimations. Identification of the stress regime, which is an imperative basis for the geomechanical modeling, can significantly alter the reservoir production scheme. Moreover, knowledge of the stress regime directly impacts the efficiency of hydraulic fracturing procedures and wellbore stability. For example, in case of reverse stress regime, the hydraulic fracturing operations could be inefficient due to the problems with the fracture initiation, high injection pressures, and risks associated with the proppant fallout in the wellbore. Fields experiencing hydraulic fracturing problems should be assessed via the geomechanical frame of reference for the comprehensive understanding of the issue. Assessing the state of stress is challenged by the absence of direct measurement tools of maximum horizontal stress. Application of the stress estimation methods commonly used in the industry (including the breakout width, acoustic anisotropy inversion and poroelastic modeling with the assumption of tectonic coefficients) have certain limitations which often lead to a broad range of obtained values of maximum horizontal stress, thus adding uncertainty to the drilling and hydraulic fracturing recommendations. Thus, the main goal of this work is to develop and apply an instrument for qualitative assessment of stress regime and direction. The reliable mathematical model, built upon the minimal set of required data, which is able to forecast the rock behavior under far-field and near wellbore stresses can be an extremely useful instrument for effective operations of drilling, fracturing, well placement and reservoir development. The underlying method for the development of the stress inversion algorithm was based on limiting the range of possible values of horizontal stresses using Anderson's definition of stress regimes, the frictional theory of Mohr-Coulomb and Kirch equations. The subsequent analysis of the breakout azimuths at the wellbore walls of several inclined wells from the image log data results in a reliable prediction of reservoir stress regime and direction. The optimal usage of the method required knowledge of vertical stress and the borehole failures logged in the deviated wells with the inclination of at least 20° and varying azimuthal directions. The developed algorithm of the improved identification of stress regime was then applied for a real field case in order to understand the geomechanical roots of the problems experienced during hydraulic fracturing treatment. Learning the stress regime and the orientation of the horizontal stresses allowed building reliable geomechanical models, necessary for the optimization of the hydraulic fracturing program and improvement of well operating efficiency. The conclusion upon the conducted work was that the methods of horizontal stress detection should be further studied for the cases where the data is not enough (for example no deviated bores logged). Moreover since all methods of SHmax estimation are inverse, the most value can be brought only by creating a tool where all the techniques (existing and the ones under development) are integrated.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,003 |
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 ».