PetrophysicsPetrophysics (from the Greek πέτρα, petra, "rock" and φύσις, physis, "nature") is the study of physical and chemical rock properties and their interactions with fluids. A major application of petrophysics is in studying reservoirs for the hydrocarbon industry. Petrophysicists work together with reservoir engineers and geoscientists to understand the porous media properties of the reservoir. Particularly how the pores are interconnected in the subsurface, controlling the accumulation and migration of hydrocarbons.
Geological formationA geological formation, or simply formation, is a body of rock having a consistent set of physical characteristics (lithology) that distinguishes it from adjacent bodies of rock, and which occupies a particular position in the layers of rock exposed in a geographical region (the stratigraphic column). It is the fundamental unit of lithostratigraphy, the study of strata or rock layers. A formation must be large enough that it can be mapped at the surface or traced in the subsurface.
Poisson point processIn probability, statistics and related fields, a Poisson point process is a type of random mathematical object that consists of points randomly located on a mathematical space with the essential feature that the points occur independently of one another. The Poisson point process is often called simply the Poisson process, but it is also called a Poisson random measure, Poisson random point field or Poisson point field.
Formation evaluationIn petroleum exploration and development, formation evaluation is used to determine the ability of a borehole to produce petroleum. Essentially, it is the process of "recognizing a commercial well when you drill one". Modern rotary drilling usually uses a heavy mud as a lubricant and as a means of producing a confining pressure against the formation face in the borehole, preventing blowouts. Only in rare and catastrophic cases, do oil and gas wells come in with a fountain of gushing oil.
Unsupervised learningUnsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.
ClayClay is a type of fine-grained natural soil material containing clay minerals (hydrous aluminium phyllosilicates, e.g. kaolinite, Al2Si2O5(OH)4). Clays develop plasticity when wet but can be hardened through firing. Most pure clay minerals are white or light-coloured, but natural clays show a variety of colours from impurities, such as a reddish or brownish colour from small amounts of iron oxide. Clay is the oldest known ceramic material. Prehistoric humans discovered the useful properties of clay and used it for making pottery.
Field extensionIn mathematics, particularly in algebra, a field extension is a pair of fields such that the operations of K are those of L restricted to K. In this case, L is an extension field of K and K is a subfield of L. For example, under the usual notions of addition and multiplication, the complex numbers are an extension field of the real numbers; the real numbers are a subfield of the complex numbers. Field extensions are fundamental in algebraic number theory, and in the study of polynomial roots through Galois theory, and are widely used in algebraic geometry.
K-means clusteringk-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances.
PorosityPorosity or void fraction is a measure of the void (i.e. "empty") spaces in a material, and is a fraction of the volume of voids over the total volume, between 0 and 1, or as a percentage between 0% and 100%. Strictly speaking, some tests measure the "accessible void", the total amount of void space accessible from the surface (cf. closed-cell foam). There are many ways to test porosity in a substance or part, such as industrial CT scanning.
Dynamic programmingDynamic programming is both a mathematical optimization method and an algorithmic paradigm. The method was developed by Richard Bellman in the 1950s and has found applications in numerous fields, from aerospace engineering to economics. In both contexts it refers to simplifying a complicated problem by breaking it down into simpler sub-problems in a recursive manner. While some decision problems cannot be taken apart this way, decisions that span several points in time do often break apart recursively.
Cluster analysisCluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.
Field (mathematics)In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined and behave as the corresponding operations on rational and real numbers do. A field is thus a fundamental algebraic structure which is widely used in algebra, number theory, and many other areas of mathematics. The best known fields are the field of rational numbers, the field of real numbers and the field of complex numbers.
Chinle FormationThe Chinle Formation is an Upper Triassic continental geological formation of fluvial, lacustrine, and palustrine to eolian deposits spread across the U.S. states of Nevada, Utah, northern Arizona, western New Mexico, and western Colorado. In New Mexico, it is often raised to the status of a geological group, the Chinle Group. Some authors have controversially considered the Chinle to be synonymous to the Dockum Group of eastern Colorado and New Mexico, western Texas, the Oklahoma panhandle, and southwestern Kansas.
Reservoir modelingIn the oil and gas industry, reservoir modeling involves the construction of a computer model of a petroleum reservoir, for the purposes of improving estimation of reserves and making decisions regarding the development of the field, predicting future production, placing additional wells and evaluating alternative reservoir management scenarios. A reservoir model represents the physical space of the reservoir by an array of discrete cells, delineated by a grid which may be regular or irregular.
Effective porosityEffective porosity is most commonly considered to represent the porosity of a rock or sediment available to contribute to fluid flow through the rock or sediment, or often in terms of "flow to a borehole". Porosity that is not considered "effective porosity" includes water bound to clay particles (known as bound water) and isolated "vuggy" porosity (vugs not connected to other pores). The effective porosity is of great importance in considering the suitability of rocks or sediments as oil or gas reservoirs, or as aquifers.
Finite fieldIn mathematics, a finite field or Galois field (so-named in honor of Évariste Galois) is a field that contains a finite number of elements. As with any field, a finite field is a set on which the operations of multiplication, addition, subtraction and division are defined and satisfy certain basic rules. The most common examples of finite fields are given by the integers mod p when p is a prime number. The order of a finite field is its number of elements, which is either a prime number or a prime power.
Scattering parametersScattering parameters or S-parameters (the elements of a scattering matrix or S-matrix) describe the electrical behavior of linear electrical networks when undergoing various steady state stimuli by electrical signals. The parameters are useful for several branches of electrical engineering, including electronics, communication systems design, and especially for microwave engineering. The S-parameters are members of a family of similar parameters, other examples being: Y-parameters, Z-parameters, H-parameters, T-parameters or ABCD-parameters.
Dynamic loadingDynamic loading is a mechanism by which a computer program can, at run time, load a library (or other ) into memory, retrieve the addresses of functions and variables contained in the library, execute those functions or access those variables, and unload the library from memory. It is one of the 3 mechanisms by which a computer program can use some other software; the other two are static linking and dynamic linking.
Impedance parametersImpedance parameters or Z-parameters (the elements of an impedance matrix or Z-matrix) are properties used in electrical engineering, electronic engineering, and communication systems engineering to describe the electrical behavior of linear electrical networks. They are also used to describe the small-signal (linearized) response of non-linear networks. They are members of a family of similar parameters used in electronic engineering, other examples being: S-parameters, Y-parameters, H-parameters, T-parameters or ABCD-parameters.
Admittance parametersAdmittance parameters or Y-parameters (the elements of an admittance matrix or Y-matrix) are properties used in many areas of electrical engineering, such as power, electronics, and telecommunications. These parameters are used to describe the electrical behavior of linear electrical networks. They are also used to describe the small-signal (linearized) response of non-linear networks. Y parameters are also known as short circuited admittance parameters.
