Estimating Mature Plays

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.

Microstructural and Magnetic Properties of Core-Shell Ni-Ce Nanocomposite Particles Assemblies

Novel magnetic core-shell Ni-Ce nanocomposite particles (15-50 nm) are presented. SEM observation indicates a strongly ferromagnetic interacting order with chain-like features among Ni-Ce nanocomposite particle assemblies. Typical HREM image demonstrates that many planar defects (i. e. stacking faults) exist in large Ni core zone (10-45 nm ); the innermost NiCe alloy and outermost NiO oxide exist in the thin shell layers ( 3-5 nm ). Nano-diffraction patterns show an indication of well-defined spots characteristic and confirm the nature of this core-shell nanocomposite particles. Superparamagnetic relaxation behavior above average blocking temperature (TB =170K) for Ni-Ce nanocomposite particles assemblies have been exhibited, this superparamagnetic behavior is found to be modified by interparticle interactions, which depending on the applied field; size distribution and coupling with the strong interparticle interaction. In addition, an antiferromagnetic order occurs with a Neél temperature TN of about 11K due to Ce ion magnetic order fuction. A spin-flop transition is also observed below TN at a certain applied field and low temperature.