The loghyperbolic: An alternative to the lognormal for modeling oil field size distribution

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

MRS Proceedings ◽

10.1557/proc-635-c6.8 ◽

2001 ◽

Vol 635 ◽

Author(s):

Xiang-Cheng Sun ◽

J. A. Toledo ◽

M. Jose Yacaman

Keyword(s):

Applied Field ◽

Magnetic Core ◽

Field Size ◽

Blocking Temperature ◽

Antiferromagnetic Order ◽

Core Shell ◽

Interparticle Interactions ◽

Nanocomposite Particles ◽

Diffraction Patterns ◽

Field Size Distribution

AbstractNovel 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.

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A Trapping Hele-Shaw Model for Miscible-Immiscible flooding Studies

Society of Petroleum Engineers Journal ◽

10.2118/4105-pa ◽

1973 ◽

Vol 13 (05) ◽

pp. 255-256

Author(s):

E.L. Claridge

Keyword(s):

Parallel Plate ◽

Recovery Process ◽

Oil Field ◽

Field Size ◽

Plate Model ◽

Miscible Displacements ◽

Plate Spacing ◽

Scale Down ◽

Rectangular Groove ◽

Surface Tension Forces

It has been noted previously that Hele-Shaw (parallel-plate) models are better than other types of laboratory models to properly scale down miscible displacements from field size to laboratory size. In most miscible flooding processes, however, the miscible displacement is preceded or followed by an immiscible displacement in which oil or gas is trapped by water, or waterflood residual oil is reconnected by a miscible slug. This trapping and reconnection could not be simulated in a conventional parallel-plate model. parallel-plate model. Now, however, a new version of this type of model has been invented that simulates the trapping behavior of porous rock. Instead of trapping by capillary (surface tension) forces, the new model traps light fluids by density difference (Fig. 1). A model of this type can be used to simulate, for example, the tertiary recovery process in which a solvent slug (e.g., CO2) injected in a waterflooded oil field and then followed by another water drive. The particular model devised for this purpose was made of 3-in. thick, 14-in. square plates purpose was made of 3-in. thick, 14-in. square plates of Plexiglas. The top plate contained 596 pairs of 1/40-in.-diameter holes, 112 in. apart at the base, and meeting 3/4 in. deep in the plate. Vent holes 1/16in. in diameter were drilled from the other side of the plate to the junctions of these pairs. The 12- x 12-in. square perforated region was supplied with wells in a nine-spot pattern, and was sealed around the periphery by an O-ring in a rectangular groove. The plates were held a fixed distance apart by shims and bolts outside the O-ring. A typical plate spacing was 0.01 cm. After the model was filled with liquids and air was forced out of the trapping holes, the vent holes were plugged with rods. P. 255

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