*P.J. Lee*

*Jo Anne DeGraffenreid (ed.)*

- Published in print:
- 2008
- Published Online:
- November 2020
- ISBN:
- 9780195331905
- eISBN:
- 9780197562550
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780195331905.003.0009
- Subject:
- Earth Sciences and Geography, Geophysics: Earth Sciences

A key objective in petroleum resource evaluation is to estimate oil and gas pool size (or field size) or oil and gas joint probability distributions for a particular population or play. The ...
More

A key objective in petroleum resource evaluation is to estimate oil and gas pool size (or field size) or oil and gas joint probability distributions for a particular population or play. The pool-size distribution, together with the number-of-pools distribution in a play can then be used to predict quantities such as the total remaining potential, the individual pool sizes, and the sizes of the largest undiscovered pools. These resource estimates provide the fundamental information upon which petroleum economic analyses and the planning of exploration strategies can be based. The estimation of these types of pool-size distributions is a difficult task, however, because of the inherent sampling bias associated with exploration data. In many plays, larger pools tend to be discovered during the earlier phases of exploration. In addition, a combination of attributes, such as reservoir depth and distance to transportation center, often influences the order of discovery. Thus exploration data cannot be considered a random sample from the population. As stated by Drew et al. (1988), the form and specific parameters of the parent field-size distribution cannot be inferred with any confidence from the observed distribution. The biased nature of discovery data resulting from selective exploration decision making must be taken into account when making predictions about undiscovered oil and gas resources in a play. If this problem can be overcome, then the estimation of population mean, variance, and correlation among variables can be achieved. The objective of this chapter is to explain the characterization of the discovery process by statistical formulation. To account for sampling bias, Kaufman et al. (1975) and Barouch and Kaufman (1977) used the successive sampling process of the superpopulation probabilistic model (discovery process model) to estimate the mean and variance of a given play. Here we shall discuss how to use superpopulation probabilistic models to estimate pool-size distribution. The models to be discussed include the lognormal (LDSCV), nonparametric (NDSCV), lognormal/nonparametric-Poisson (BDSCV), and the bivariate lognormal, multivariate (MDSCV) discovery process methods. Their background, applications, and limitations will be illustrated by using play data sets from the Western Canada Sedimentary Basin as well as simulated populations.
Less

A key objective in petroleum resource evaluation is to estimate oil and gas pool size (or field size) or oil and gas joint probability distributions for a particular population or play. The pool-size distribution, together with the number-of-pools distribution in a play can then be used to predict quantities such as the total remaining potential, the individual pool sizes, and the sizes of the largest undiscovered pools. These resource estimates provide the fundamental information upon which petroleum economic analyses and the planning of exploration strategies can be based. The estimation of these types of pool-size distributions is a difficult task, however, because of the inherent sampling bias associated with exploration data. In many plays, larger pools tend to be discovered during the earlier phases of exploration. In addition, a combination of attributes, such as reservoir depth and distance to transportation center, often influences the order of discovery. Thus exploration data cannot be considered a random sample from the population. As stated by Drew et al. (1988), the form and specific parameters of the parent field-size distribution cannot be inferred with any confidence from the observed distribution. The biased nature of discovery data resulting from selective exploration decision making must be taken into account when making predictions about undiscovered oil and gas resources in a play. If this problem can be overcome, then the estimation of population mean, variance, and correlation among variables can be achieved. The objective of this chapter is to explain the characterization of the discovery process by statistical formulation. To account for sampling bias, Kaufman et al. (1975) and Barouch and Kaufman (1977) used the successive sampling process of the superpopulation probabilistic model (discovery process model) to estimate the mean and variance of a given play. Here we shall discuss how to use superpopulation probabilistic models to estimate pool-size distribution. The models to be discussed include the lognormal (LDSCV), nonparametric (NDSCV), lognormal/nonparametric-Poisson (BDSCV), and the bivariate lognormal, multivariate (MDSCV) discovery process methods. Their background, applications, and limitations will be illustrated by using play data sets from the Western Canada Sedimentary Basin as well as simulated populations.

*P.J. Lee*

*Jo Anne DeGraffenreid (ed.)*

- Published in print:
- 2008
- Published Online:
- November 2020
- ISBN:
- 9780195331905
- eISBN:
- 9780197562550
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780195331905.003.0008
- Subject:
- Earth Sciences and Geography, Geophysics: Earth Sciences

The initial step in the evaluation of any petroleum resource is the identification of an appropriate geological population that can be delineated through subsurface study or basin analysis. A ...
More

The initial step in the evaluation of any petroleum resource is the identification of an appropriate geological population that can be delineated through subsurface study or basin analysis. A geological population represents a natural population and possesses a group of pools and/or prospects sharing common petroleum habitats. A natural population can be a single sedimentation model, structural style, type of trapping mechanism or geometry, tectonic cycle, stratigraphic sequence, or any combination of these criteria. Reasons for adopting these criteria in the definition of a geological model are the following: • The geological population will be defined clearly and its associated resource can readily be estimated. • Geologists can adopt known play data for future comparative geological studies. • Geological variables of a natural population can be described by probability distributions (e.g., the lognormal distribution). Statistical concepts such as the superpopulation concept can be applied to geological models so that, for specific plays, an estimate of undiscovered pool sizes can be made. Figure 2.1 illustrates various sedimentary environments (tidal flat, lagoon, beach, and patch reef) that can be used as geological models in resource evaluation. Each of these models has its own distinguishing characteristics of source, reservoir, trapping mechanism, burial and thermal history of source beds, and migration pathway. In resource evaluation, to ensure the integrity of statistical analysis, each of these should be treated as a separate, natural population. Therefore, the logical steps in describing a play are (1) identify a single sedimentation model and (2) examine subsequent geological processes. Geological processes such as faulting, erosion, folding, diagenesis, biodegradation, thermal history of source rocks, and migration history might provide a basis for further subdivisions of the model. In some cases, two or more populations might be considered mistakenly as a single population because of a lack of understanding of the subsurface geology. If the resulting mixed population were to have two or more modes in its distribution, this could have an impact on resource evaluation results.
Less

The initial step in the evaluation of any petroleum resource is the identification of an appropriate geological population that can be delineated through subsurface study or basin analysis. A geological population represents a natural population and possesses a group of pools and/or prospects sharing common petroleum habitats. A natural population can be a single sedimentation model, structural style, type of trapping mechanism or geometry, tectonic cycle, stratigraphic sequence, or any combination of these criteria. Reasons for adopting these criteria in the definition of a geological model are the following: • The geological population will be defined clearly and its associated resource can readily be estimated. • Geologists can adopt known play data for future comparative geological studies. • Geological variables of a natural population can be described by probability distributions (e.g., the lognormal distribution). Statistical concepts such as the superpopulation concept can be applied to geological models so that, for specific plays, an estimate of undiscovered pool sizes can be made. Figure 2.1 illustrates various sedimentary environments (tidal flat, lagoon, beach, and patch reef) that can be used as geological models in resource evaluation. Each of these models has its own distinguishing characteristics of source, reservoir, trapping mechanism, burial and thermal history of source beds, and migration pathway. In resource evaluation, to ensure the integrity of statistical analysis, each of these should be treated as a separate, natural population. Therefore, the logical steps in describing a play are (1) identify a single sedimentation model and (2) examine subsequent geological processes. Geological processes such as faulting, erosion, folding, diagenesis, biodegradation, thermal history of source rocks, and migration history might provide a basis for further subdivisions of the model. In some cases, two or more populations might be considered mistakenly as a single population because of a lack of understanding of the subsurface geology. If the resulting mixed population were to have two or more modes in its distribution, this could have an impact on resource evaluation results.