Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Earth science
Reexamination Certificate
2000-02-11
2003-04-15
Picard, Leo (Department: 2125)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Earth science
C702S005000, C702S006000, C367S073000
Reexamination Certificate
active
06549854
ABSTRACT:
BACKGROUND OF THE INVENTION
This invention is related to subsurface modeling, and is more particularly concerned with a parametric subsurface modeling method, apparatus, and article of manufacture that use uncertainty estimates of subsurface model parameters.
Subsurface models are typically created by geoscientists and engineers to allow development strategies for the subsurface area to be evaluated. Models of this type are commonly created in connection with the development of hydrocarbon reservoirs and mining sites, but they can also used during drilling and related activities where the physical properties of the subsurface area are important. This patent application will focus on the process of creating and updating a model of a subsurface hydrocarbon reservoir, but it should be understood that this merely represents one specific example of how a model of any subsurface area may be created and updated.
Currently, hydrocarbon reservoir modeling is performed most commonly in high-risk, high-profile situations. Typical applications include discoveries in new areas, deepwater exploration, fields in which production surprises or drilling hazards have been encountered, fields in which secondary and tertiary recovery activities are planned, and fields which are being considered for sale or abandonment. The failure to adequately model hydrocarbon reservoirs can have numerous adverse financial consequences, including inaccurate reserve calculations, drilling or completion problems, improper production facility sizing, and suboptimal well placement.
The general problem addressed by this invention is how to construct a model of a subsurface area that is in agreement with multiple sets of measurement data. A model that is in agreement with all of the measurement data obtained from the reservoir can help address many of the problems noted above. By ‘reservoir model’ we mean a quantitative parameterized representation of the subsurface in terms of geometries and material properties. The geometrical model parameters will typically identify geological boundaries, such as contacts between different geologic layers, faults, or fluid/fluid interfaces. The material model parameters will typically identify properties of distributed subsurface materials, such as seismic wave velocities, porosities, permeabilities, fluid saturations, densities, fluid pressures, or temperatures.
By ‘agreement’ we mean that the data predicted from the reservoir model fit measurements made on the actual reservoir (seismic data, drilling data, well logging data, well test data, production history data, permanent monitoring data, ground penetrating radar data, gravity measurements, etc.). Virtually all types of measurement data have quantifiable uncertainties and the reservoir model agrees with the measurement data when the difference between data predicted by the reservoir model and measurement data obtained from the reservoir is less than this inherent measurement uncertainty. While creating a reservoir model that fits one data set is a relatively straightforward task, it is much more difficult to ensure that the model is in agreement with multiple data sets, particularly if the data sets consist of different types of data.
A reservoir model, however, is nonunique even if it is made to fit a variety of data, because different values of material properties and geometries within the model can result in similar predicted measurement values. In other words, the reservoir model has inherent uncertainties: each of the numerical parameters in the reservoir model (e.g., values of material properties within a layer) can take a range of values while the model remains in agreement with the data. This range in parameter values is the uncertainty associated with the reservoir model. The invention described herein is a method to integrate information from multiple measurements and to obtain a reservoir model with quantitative uncertainties in the model parameters. A model of the reservoir that fits the data and has quantified uncertainties can be used to assess the risk inherent in reservoir development decisions (e.g., deciding on the location of additional wells) and to demonstrate the value of additional measurements by showing how these measurements decrease uncertainties in model parameters of interest (e.g., the location of a drilling target or hazard).
A Shared Earth Model (SEM) is a geometrical and material property model of a subsurface area. The model is shared, in the sense that it integrates the work of several experts (geologists, geophysicists, well log analysts, reservoir engineers, etc.) who use information from a variety of measurements and interact with the model through different application programs. Ideally, the SEM contains all available information about a reservoir and thus is the basis to make forecasts and plan future actions.
Yet, in any practical case, the information in the measurements is not sufficient to uniquely constrain the parameters (geometries. and material properties) of a SEM. As noted above, any SEM has an associated uncertainty, defined here as the range that model parameters can take while fitting available measurements.
The invention has two primary aspects. The first aspect is a method to quantify and update model parameter uncertainties based on available measurements. One embodiment of this method is based on Bayes' rule, with SEM uncertainty quantified by a posterior probability density function (PDF) of the model parameters, conditioned on the measurements used to constrain the model. This posterior PDF may be approximated by a multivariate normal distribution, which is fully described by the posterior mean and covariance matrix of the SEM parameters. Alternatively, one can use a Monte Carlo method to obtain a sample of models drawn from the posterior PDF. This sample of models spans the uncertainty implied by the measurements.
The second aspect is how such a measure of uncertainty acts as a ‘memory’ of the SEM and can be used for consistent model updating. Quantified uncertainties provide a mechanism to ensure that updates of the SEM based on new data (e.g., well data) are consistent with information provided by data examined previously (e.g., surface seismic data). In particular, we show through a simple example how the effects of a local update of the model can be propagated using the posterior covariance matrix of the SEM parameters. We also show how to update a sample of models obtained by the Monte Carlo method to include new information.
The ideal of a SEM is that all specialists should be able to interact with a common geometry and material property model of the reservoir, incorporating changes into the model using measurements from their own domain of expertise, while maintaining model consistency with previous measurements. This SEM representation would always be consistent with all available information and should be easy to update as soon as new measurements become available (e.g., from additional wells). Model building would not be a task done episodically, but instead the reservoir model would evolve incrementally as more and more information becomes available during development and production.
While acquiring more measurements can reduce uncertainty, it is important to weigh the cost of data acquisition against the benefits of reducing uncertainty. This can be done using the tools of decision theory, where different decisions are compared given their associated gains/costs and current uncertainties. A consistent quantification of uncertainties can assist oil companies in making data acquisition, drilling, or development decisions.
Currently, reservoir models are simply modified to fit new data and confirming that the modification is not inconsistent with the previously obtained measurement data is left up to the discretion of the user. The reservoir model may be the result of years of effort and may incorporate measurement data from a wide variety of sources. A user will often only confirm that the change made is not inconsistent with the measurement data withi
Malinverno Alberto
Prange Michael
Batzer William B.
Kosowski Alexander
Picard Leo
Ryberg John J.
Schlumberger Technology Corporation
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