Field of the Invention
This invention relates to methods and apparatus for modeling, evaluating and simulating hydrocarbon bearing subterranean formations (which are commonly referred to as reservoirs).
State of the Art
Petroleum consists of a complex mixture of hydrocarbons of various molecular weights, plus other organic compounds. The exact molecular composition of petroleum varies widely from formation to formation. The proportion of hydrocarbons in the mixture is highly variable and ranges from as much as 97% by weight in the lighter oils to as little as 50% in the heavier oils and bitumens. The hydrocarbons in petroleum are mostly alkanes (linear or branched), cycloalkanes, aromatic hydrocarbons, or more complicated chemicals like asphaltenes. The other organic compounds in petroleum typically contain carbon dioxide (CO2), nitrogen, oxygen and sulfur, and trace amounts of metals such as iron, nickel, copper and vanadium.
Petroleum is usually characterized by SARA fractionation where asphaltenes are removed by precipitation with a paraffinic solvent and the deasphalted oil separated into saturates, aromatics and resins by chromatographic separation.
The saturates include alkanes and cycloalkanes. The alkanes, also known as paraffins, are saturated hydrocarbons with straight or branched chains which contain only carbon and hydrogen and have the general formula CnH2n+2. They generally have from 5 to 40 carbon atoms per molecule, although trace amounts of shorter or longer molecules may be present in the mixture. The alkanes include methane (CH4), ethane (C2H6), propane (C3H8), i-butane (iC4H10), n-butane (nC4H10), i-pentane (iC5H12), n-pentane (nC5H12), hexane (C6H14), heptane (C7H16), octane (C8H18), nonane (C9H20), decane (C10H22), hendecane (C11H24)—also referred to as endecane or undecane, dodecane (C12H26), tridecane (C13H28), tetradecane (C14H30), pentadecane (C15H32) and hexadecane (C16H34). The cycloalkanes, also known as napthenes, are saturated hydrocarbons which have one or more carbon rings to which hydrogen atoms are attached according to the formula Cycloalkanes have similar properties to alkanes but have higher boiling points. The cycloalkanes include cyclopropane (C3H6), cyclobutane (C4H8), cyclopentane (C5H10), cyclohexane (C6H12), cycloheptane (C7H14), etc.
The aromatic hydrocarbons are unsaturated hydrocarbons which have one or more planar six-carbon rings called benzene rings, to which hydrogen atoms are attached with the formula CnHn. They tend to burn with a sooty flame, and many have a sweet aroma. Some are carcinogenic. The aromatic hydrocarbons include benzene (C6H6) and derivatives of benzene as well as polyaromatic hydrocarbons.
Resins are the most polar and aromatic species present in the deasphalted oil and, it has been suggested, contribute to the enhanced solubility of asphaltenes in crude oil by solvating the polar and aromatic portions of the asphaltenic molecules and aggregates.
Asphaltenes are insoluble in n-alkanes (such as n-pentane or n-heptane) and soluble in toluene. The C:H ratio is approximately 1:1.2, depending on the asphaltene source. Unlike most hydrocarbon constituents, asphaltenes typically contain a few percent of other atoms (called heteroatoms), such as sulfur, nitrogen, oxygen, vanadium and nickel. Heavy oils and tar sands contain much higher proportions of asphaltenes than do medium-API oils or light oils. Condensates are virtually devoid of asphaltenes. As far as asphaltene structure is concerned, experts agree that some of the carbon and hydrogen atoms are bound in ring-like, aromatic groups, which also contain the heteroatoms. Alkane chains and cyclic alkanes contain the rest of the carbon and hydrogen atoms and are linked to the ring groups. Within this framework, asphaltenes exhibit a range of molecular weight and composition. Asphaltenes have been shown to have a distribution of molecular weight in the range of 300 to 1400 g/mol with an average of about 750 g/mol. This is compatible with a molecule contained seven or eight fused aromatic rings, and the range accommodates molecules with four to tens rings. It is also known that asphaltene molecules aggregate to form nanoaggregates and clusters.
The life cycle of a reservoir typically follows certain stages including, but not limited to exploration, assessment, reservoir development, production, decline, and abandonment of the reservoir. Important decisions must be made at each of these stages in order to properly allocate resources and to assure that the reservoir meets its production potential. In the early stages of the life cycle, one begins with almost complete ignorance about the distribution of the internal properties within the reservoir. As development continues, diverse types of reservoir data are collected, such as seismic, well logs, and production data. Such reservoir data are combined to construct an understanding of the reservoir.
Computer-based software applications are commercially available for generating geological models which predict and describe the rock properties and features of subterranean formation. For example, geological models are built from data acquired during the exploration stage, such as seismic analysis, formation evaluation logs, and pressure measurements. Fluid models are built with the input from lab pressure-volume-temperature (PVT) analyses, geochemistry studies, pressure gradients, and downhole fluid analysis (DFA). Fluid models can be combined with geological models as part of a reservoir simulation grid (also commonly referred to as a reservoir model). The reservoir simulation grid represents the three-dimensional physical space of the formation by an array of discrete cells, delineated by a grid system which may be regular or irregular. Values for rock properties (e.g., porosity, permeability, water saturation) and fluid properties (e.g., compositions of liquid and gaseous phases, pressure, and temperature) are associated with each cell. Equations and associated computations are used to model and simulate the flow of fluids during production. Uncertainty in the values of the rock and fluid properties of the reservoir can be investigated by constructing several different realizations of the sets of property values. The phrase “reservoir characterization” is sometimes used to refer to reservoir modeling activities up to the point where the reservoir simulation grid characterizes the static rock and fluid properties of the reservoir, i.e., before the simulation of the dynamic flow of fluids during production.
Such computer-based reservoir modeling applications are used to achieve a better understanding of the reservoir and make critical decisions with respect to reservoir development. However, prior to the reservoir development stage, the uncertainty in these models is relatively high. Consequently, known reservoir modeling applications are not always available with sufficient accuracy to permit efficient reservoir development. This is a problem because relatively greater risk exists in the reservoir development stage in comparison with the exploration and assessments stages. Activity tends to occur at a faster pace in the reservoir development stage. For example, an operator typically decides which zones are to be completed immediately after logging and sampling operations. The zones are selected based on predicted commercial value as indicated by the volume of reserves represented in the existing models. If a mistake is made because of model inaccuracy, a costly workover operation and delayed production may result. The risks are particularly high in the case of offshore development because of higher development and operating costs.
One particular impediment to efficient reservoir development is reservoir compartmentalization. Reservoir compartmentalization is the natural occurrence of hydraulically isolated pockets within a reservoir. In order to produce a reservoir in an efficient manner, it is necessary to know the structure of the rock and the level of compartmentalization. A reservoir compartment does not produce unless it is tapped by a well. In order to justify the drilling of a well, the reservoir compartment must be sufficiently large to sustain economic production. Furthermore, in order to achieve efficient recovery, it is generally desirable to know the locations of as many of the reservoir compartments as practical before extensive development has been done.
There are three industry standard procedures widely used to understand reservoir compartmentalization. First is the evaluation of petrophysical logs. Petrophysical logs may identify impermeable barriers, and the existence of such barriers can be taken to mean that the reservoir is compartmentalized. Examples include gamma ray and NMR logs, both of which can identify impermeable barriers in favorable situations. Another example is the evaluation of mud filtrate invasion monitored by resistivity logs. However, impermeable barriers may be so thin that they are not observable by these logs, or barriers observed by these logs may not extend away from the wellbore and therefore may not compartmentalize the reservoir. Second is the evaluation of pressure gradients. If two permeable zones are not in pressure communication, they are not in flow communication. However, the presumption that pressure communication implies flow communication has repeatedly been proven to be incorrect. Pressure equilibration requires relatively little fluid flow and can occur more than 5 orders of magnitude faster than fluid compositional equilibration, even in the presence of flow barriers. Continuous pressure gradients are a necessary but insufficient test for reservoir connectivity. Third is the comparison of geochemical fingerprints of fluid samples acquired from different locations in the reservoir. Petroleum is a complex chemical mixture, containing many different chemical compounds; the composition of that petroleum can therefore be treated as a fingerprint. If the composition of petroleum samples from two different places in the reservoir is the same, it is assumed that fluids can flow readily between those two places in the reservoir and hence that the reservoir is connected. However, forces such as biodegradation and water washing can occur to different extents in different parts of the reservoir, causing two places in the reservoir to have different fingerprints even if they are connected. Additionally, petroleum samples generated from the same source rock may have very similar fingerprints even if they come from locations in the reservoir that are presently disconnected.
An alternative method to assess connectivity is to evaluate hydrocarbon fluid compositional grading. The chemical composition of petroleum must be different in different parts of a connected reservoir. This change in composition with position (typically depth) in the reservoir is referred to as compositional grading. The magnitude of this compositional grading (i.e., the difference in the composition of two fluids collected from different depths), in connected reservoirs at thermodynamic equilibrium, can be modeled with mathematical equations of state (EOS) and measured with downhole fluid analysis. If the magnitude of compositional grading is measured, and the measurement matches the predictions of the model, then the assumptions of the model are believed to be correct. In this case, the assumptions are that the reservoir is connected and at thermodynamic equilibrium. In the event that the magnitude of the compositional grading does not match the predictions of the EOS model, it can be assumed that there is reservoir compartmentalization or that the reservoir fluids are not in equilibrium. Many different forces can contribute to a lack of thermodynamic equilibrium, such as tar mats, water washing, biodegradation, real-time charging, etc. It can be difficult to determine whether the reservoir is compartmentalized or not in thermodynamic equilibrium, and this determination can be critical to important development decisions. More specifically, the traditional EOS (such as the Peng-Robinson EOS developed in 1976) utilized for compositional grading analysis are derived by adding correction terms to the ideal gas law to address gas-liquid equilibria. Thus, these standard EOS allow for compositional analysis of only gas and liquid phase fractions of the reservoir fluid, and such limited information makes it difficult to determine whether the reservoir is compartmentalized or not in thermodynamic equilibrium.
Thus, there is a clear need for methodologies that provide for an effective understanding of reservoir compartmentalization as early as possible (e.g., before development) in the lifecycle of the reservoir.