Simulating the Spatial Distribution of Undiscovered Petroleum Accumulations
Notice bibliographique
Résumé
Abstract There are two major problems in construction of a stochastic model that describes quantitatively the spatial distribution of undiscovered petroleum accumulations:available exploration results are biased andinformation associated with the locations of accumulations is incomplete. Studies in the Western Canada Sedimentary Basin (WCSB) and elsewhere indicate that the spatial characteristics of petroleum accumulations are fractal. In this paper we propose the use of these fractal characteristics to calibrate sampling bias, thus deriving an unbiased spatial correlation (covariance function) for the stochastic modeling. The uncertainty in the modeled locations of undiscovered accumulations resulting from insufficient information is captured by equal-probable realizations of the simulation and these are subsequently converted to a probability map of petroleum occurrence. In the example, a pre-1994 exploratory data set for the Rainbow gas play in WCSB was used to derive simulation parameters. A comparison of the simulated results to post-1993 gas discoveries in the same play shows that most of the post-1993 discoveries are located in areas with high predicted probability values. Introduction Spatial characteristics of undiscovered oil and gas accumulations are important for both better natural resource management and improved exploration efficiency. There are two major obstacles associated with the construction of a stochastic model for describing the spatial distribution of undiscovered petroleum accumulations. The first is that the available information is biased with respect to exploration results. The second is that information associated with the locations of petroleum accumulations is incomplete unless all accumulations are discovered. It is well known that the data associated with the discovery of petroleum accumulations in an exploration program is biased. Larger features are generally tested with higher priority (1,2). This sampling bias prohibits the use of conventional methods to estimate stochastic model parameters. Barton et al. (3) and La Pointe (4) studied the data from well-explored petroleum basins in the United States and concluded that the spatial distribution of hydrocarbon accumulations is fractal. Our studies in WCSB also indicate that the spatial distribution of petroleum accumulations exhibits a self-similar characteristic. Figure 1 shows the box counting results for the Rainbow gas play in WCSB. The linear relation between box size and the number of boxes containing gas pools (on a logarithmic scale) indicates fractal geometry of the spatial distribution of gas accumulations. The scaling property of the spatial objects means that spatial characteristics of large objects of petroleum accumulations could be used to infer the spatial characteristics for the smaller ones which are underrepresented in the data set. Thus an unbiased spatial structure of petroleum accumulations could be inferred from the biased observations. By transforming the spatial information of discovered hydrocarbon accumulations into a frequency domain using a fast Fourier transform (FFT), it results in an amplitude map and a phase map. The amplitude map contains information associated with spatial correlation, while the phase map contains location-specific information. When both the location specific information in phase map and the spatial correlation in the amplitude map are complete, the true spatial locations of petroleum accumulation can be inferred.
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Comment cette classification a été obtenuedéplier
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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».