Schlumberger Technology Corporation

Alberto Malinverno

Dzevat Omeragic

Richard Timothy Coates

Ian D Bryant

Michael David Prange

David J Rossi

Min-Yi Chen

Nicholas N Bennett

Carlos Torres-Verdin

Metin Karakas

Brian Spies

A computer-based method is provided for modeling and visualizing a property of a subterranean earth formation while drilling a borehole therethrough. The computer-based method gathers electromagnetic signals corresponding to a current measurement station location of a measurement-while-drilling tool, and generates a multilayer model corresponding to such electromagnetic signals. A histogram characterizing uncertainty of the multilayer model is used to generate a set of color hue values which represent predictions of the formation property for depth values above/below the tool, and a corresponding set of saturation values (which represent uncertainties for these predictions). A curtain plot is generated and displayed. The curtain plot employs colors to visualize formation property predictions for depth values above/below the tool over successive measurement station locations. A new column of the curtain plot is generated for the current measurement station location. The color values of the new column are based upon the set of color hue values and the set of saturation values derived from the histogram. The saturation levels of the new column represent uncertainties for the corresponding predictions.

Computer-based method for while-drilling modeling and visualization of layered subterranean earth formations

A method of predicting values of formation parameters (e.g., compressional velocity, density, pore pressure, and fracture pressure) as a function of depth includes generating an initial prediction of a profile of the formation parameters and uncertainties associated therewith using information available regarding the formation, obtaining information related to the formation parameters during drilling, and updating the uncertainties as a function of the first prediction and the information obtained in a recursive fashion. Known equations are used for finding initial values, and uncertainties associated therewith are quantified by using probability density functions (PDFs). A Bayesian approach is utilized where &ldquo;prior PDFs&rdquo; describe uncertainty prior to obtaining additional information, and &ldquo;posterior PDFs&rdquo; account for the additional information acquired. As additional information is acquired while drilling, the posterior PDFs are redefined. Uncertainty in the formation parameters is quantified by sampling posterior PDFs given all the data with a Markov Chain Monte Carlo algorithm which generates numerous formation parameter profiles consistent with the data and the computed Bayesian uncertainties. Histograms of the numerous formation parameter profiles may be plotted to visualize the uncertainty in the formation parameters.

Methods and apparatus for predicting pore and fracture pressures of a subsurface formation

A method, apparatus, and article of manufacture are provided that use measurement data to create a model of a subsurface area. The method includes creating an initial parameterized model having an initial estimate of model parameter uncertainties; considering measurement data from the subsurface area; updating the model and its associated uncertainty estimate; and repeating the considering and updating steps with additional measurement data. A computer-based apparatus and article of manufacture for implementing the method are also disclosed. The method, apparatus, and article of manufacture are particularly useful in assisting oil companies in making hydrocarbon reservoir data acquisition, drilling and field development decisions.

Uncertainty constrained subsurface modeling

Methods for locating an oil-water interface in a petroleum reservoir include taking resistivity and pressure measurements over time and interpreting the measurements. The apparatus of the invention includes sensors preferably arranged as distributed arrays. According to a first method, resistivity and pressure measurements are acquired simultaneously during a fall-off test. Resistivity measurements are used to estimate the radius of the water flood front around the injector well based on known local characteristics. The flood front radius and fall-off pressure measurements are used to estimate the mobility ratio. According to a second method, resistivity and pressure measurements are acquired at a variety of times. Prior knowledge about reservoir parameters is quantified in a probability density function (pdf). Applying Bayes' Theorem, prior pdfs are combined with measurement results to obtain posterior pdfs which quantify the accuracy of additional information. As new measurements are acquired, posterior pdfs, updated for expected temporal variations, become prior pdfs for the new measurements. According to a third method, uncertainty about the reservoir parameters is represented by Gaussian pdfs. The relationship between measurements and reservoir parameters is locally approximated by a linear function. Uncertainties are quantified by a posterior covariance matrix.

Methods and apparatus for dynamically estimating the location of an oil-water interface in a petroleum reservoir

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Blauvelt, NY, United States of America

Alberto Malinverno