Seismic Reservoir Modeling. Dario Grana

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Seismic Reservoir Modeling - Dario Grana


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are generally made of mixtures of minerals rather than pure minerals.

      In some applications, we might be interested in transformations of random variables, or, in other words, in the response of a function of a random variable. For example, in reservoir modeling, we might be interested in the permeability distribution, and because of the lack of direct measurements, we estimate the distribution as a function of the distribution of another random variable, for example, porosity.

Graph depicts beta probability density function in the interval [0, 1]: the solid line represents a Beta distribution with parameters alpha = beta = 0.1, whereas the dashed line represents a Beta distribution with parameters alpha = beta = 2.

      For example, if X is distributed according to a Gaussian distribution script upper N left-parenthesis upper X semicolon mu Subscript upper X Baseline comma sigma Subscript upper X Superscript 2 Baseline right-parenthesis with mean μX and variance sigma Subscript upper X Superscript 2, and we apply a linear transformation Y = g(X) = aX + b, then Y is still distributed according to a Gaussian distribution script upper N left-parenthesis upper Y semicolon mu Subscript upper Y Baseline comma sigma Subscript upper Y Superscript 2 Baseline right-parenthesis with mean μY:

      and variance sigma Subscript upper Y Superscript 2:

      If X is distributed according to a uniform distribution on the interval [c, d], XU([c, d]), and we apply a linear transformation Y = g(X) = aX + b, then Y is still distributed according to a uniform distribution YU([ac + b, ad + b]), on the interval [ac + b, ad + b].

      If the transformation is not linear, an analytical solution is not always available. A numerical method to obtain an approximation of the probability distribution of the random variable of interest is the Monte Carlo simulation approach. A Monte Carlo simulation consists of three main steps: (i) we generate a set of random samples from the known input distribution; (ii) we apply a physical transformation to each sample; and (iii) we estimate the distribution of the output variable by approximating the histogram of the computed samples. Monte Carlo simulations are often applied in geoscience studies to quantify the propagation of the uncertainty from the input data to the predicted values of a physical model.

      In many subsurface modeling problems, we cannot directly measure the properties of interest but can only collect data indirectly related to them. For example, in reservoir modeling, we cannot directly measure porosity far away from the well, but we can acquire seismic data that depend on porosity and other rock and fluid properties. Geophysics generally provides the physical models that link the unknown property, such as porosity, to the measured data, such as seismic velocities. Therefore, the estimation of the unknown model from the measured data is, from the mathematical point of view, an inverse problem.

      If m represents the unknown physical variables (i.e. the model), d represents the measurements (i.e. the data), and f is the set of physical equations (i.e. the forward operator) that links the model to the data, then the problem can be formulated as:

      where ε is the measurement error associated with the data. The data d can be a function of time and/or space, or a set of discrete observations. When m and d are vectors of size nm and nd, respectively, then f is a function from double-struck upper R Superscript n Super Subscript m to double-struck upper R Superscript n Super Subscript d. When m and d are functions, then f is an operator. The operator f can be a linear or non‐linear system of algebraic equations, ordinary or partial differential equations, or it might involve an algorithm for which there is no explicit analytical formulation. The forward problem is to compute d given m. Our focus is on the inverse problem of finding m given d and assessing the uncertainty of the predictions. In other words, we aim to predict the posterior distribution of md.