scholarly journals Description of a discovery process modeling system to forecast future oil and gas incorporating field growth; ARDS Ver. 5.01

1998 ◽  
Author(s):  
J.H. Schuenemeyer ◽  
L.J. Drew
Keyword(s):  
Process Modeling ◽  
Oil And Gas ◽  
Discovery Process ◽  
Field Growth ◽  
Discovery Process Modeling

Discovery process modeling—A sensitivity study

1994 ◽  
Vol 3 (4) ◽  
pp. 295-303 ◽  
Author(s):  
Zhuoheng Chen ◽  
Richard Sinding-Larsen
Keyword(s):  
Process Modeling ◽  
Sensitivity Study ◽  
Discovery Process ◽  
Discovery Process Modeling

Potential Hydrocarbon Resources in Aral Sea Area in the North Ustyurt Basin

2013 ◽  
Vol 734-737 ◽  
pp. 397-403
Author(s):  
Tian Wei Zhou ◽  
Jun Zhang Zheng ◽  
Ling Hong Kong ◽  
Chun Sheng Wang ◽  
Ya Ping Lin ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Probabilistic Method ◽  
Aral Sea ◽  
Discovery Process ◽  
Discovery Process Modeling ◽  
The North ◽  
Oil And Gas Resources ◽  
Pool Size Distribution ◽  
Exploration Area ◽  
Sea Area

A probabilistic method named discovery process modeling is described for estimating the quantity of undiscovered oil and gas resources in Aral sea area in the North Ustrurt basin. In this model, the pool size distribution was demonstrated, and the numbers and sizes of undiscovered pools were estimated. The most likely remaining plays potential in Area sea area is 3447.2 Billions of standard cubic meters of gas in place. The eastern Jurassic-Cretaceous play bears 2901.5 Billions of standard cubic meters of undiscovered gas in place, and 17 gas pools are yet to be discovered; the paleogene-Neogene play bears 545.7 Billions of standard cubic meters of undiscovered gas in place, and 13 gas pools are yet to be discovered. Based on resources analysis, the Aral sea area is a prospecting exploration area for gas, and the emphasis should be strengthened on the eastern Jurassic-Cretaceous play.

Observed oil and gas field size distributions: A consequence of the discovery process and prices of oil and gas

1988 ◽  
Vol 20 (8) ◽  
pp. 939-953 ◽  
Author(s):  
Lawrence J. Drew ◽  
Emil D. Attanasi ◽  
John H. Schuenemeyer
Keyword(s):  
Oil And Gas ◽  
Field Size ◽  
Size Distributions ◽  
Discovery Process ◽  
Gas Field ◽  
Oil And Gas Field

The space-time structure of oil and gas field growth in a complex depositional system

1994 ◽  
Vol 3 (3) ◽  
pp. 169-182
Author(s):  
Lawrence J. Drew ◽  
Richard F. Mast ◽  
John H. Schuenemeyer
Keyword(s):  
Oil And Gas ◽  
Space Time ◽  
Time Structure ◽  
Gas Field ◽  
Field Growth ◽  
Depositional System ◽  
Space Time Structure ◽  
Oil And Gas Field

Estimating Mature Plays

2008 ◽  
Author(s):  
P.J. Lee
Keyword(s):  
Size Distribution ◽  
Oil And Gas ◽  
Joint Probability ◽  
Sampling Bias ◽  
Pool Size ◽  
Field Size ◽  
Discovery Process ◽  
Field Size Distribution ◽  
Pool Size Distribution ◽  
Economic Analyses

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.

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