A.K. Chopra

Integration of seismic attribute map into 3D facies modeling

Abstract Clastic reservoir characterization typically starts with the modeling of the facies distribution and geometry. The architecture of the reservoir, governed by the facies geometry, is a major source of heterogeneity in such clastic systems. Seismic data potentially provide valuable information about the areal distribution of different facies. However, seismic data are available only at coarse vertical resolution, more closely representing interval average rock properties, whereas well log facies data more closely represent point rock properties. This scale difference or “volume support” difference between the seismic data and the facies data available along the wells makes direct integration difficult. A recently developed algorithm based on the concept of cokriging with block average data is woven into probability field simulation for building facies models. The seismic data at its coarse vertical scale, equivalent to attribute maps, can be fully accounted for without any implicit or explicit vertical duplication to match the fine vertical scale of geologic modeling. The cpu-speed advantage of probability field simulation is also retained. The algorithm is demonstrated on a clastic petroleum reservoir. The results are compared with those obtained from facies indicator simulation without integrating seismic data and those using seismic data duplicated along the vertical direction.

Wavelet Sensitivity Study On Inversion Using Heuristic Combinatorial Algorithms

The wavelet determination is important for seismic inversion. This study investigates the wavelet sensitivity for inversion using heuristic combinatorial algorithms in the time domain. The results conclude that for inversion using heuristic combinatorial algorithms, precise knowledge of the shape of the wavelet (i. e., frequency, phase and time interval) is not necessary, even though these do help the inversion. Moreover, the frequency band of the wavelet for the new inversion strategy needs to be wider than the frequency band of the true wavelet. The phase and the length have less impact lead to a on the inversion results than the frequency, and mainly time shift of the inverted results.

Integrated Geostatistical Reservoir Description Using Petrophysical, Geological, and Seismic Data for Yacheng 13-1 Gas Field

Seismic data are routinely and effectively used to delineate the structure of a reservoir. However, the use of seismic data in reservoir modeling has been limited. This paper introduces a new approach of incorporating 3-D seismic data in reservoir porosity modeling. This approach employs a stochastic seismic inversion technique to generate the seismic impedance. The inversion technique uses a modified stochastic hillclimbing method. Correlation between porosity and the inverted impedance is established at well locations. The resulting relationship is used to generate 3-D porosity models. The generation of these models involves a stochastic co-simulation of inverted seismic impedance of log-derived porosity. This modeling technique is applied to the Yacheng 13-1 Gas Field. The results are compared with porosity models generated using well-log data only, as well as with using seismic amplitude and well-log data since a good correlation between seismic amplitude and well log data is also observed after transforming the data into similar scales. The results demonstrate a protocol for early integration of geological and geophysical data in a gas reservoir. This approach will allow easy revision and refinement of the description with additional data, such as new well data or new interpretation of the existing data.

Some Practical Considerations For Application of Heuristic Combinatorial Algorithm to Reservoir Description

The heuristic combinatorial algorithm inversion has several advantages when compared to conventional methods. However, most of the algorithms, such as GA and SA are highly computationally demanding. Even though the modified stochastic hillclimbing algorithm is fast, it also needs to be computationally efficient for further popular practical applications. In this work, a better initialization was used, that is, the well impedance was used for initialization in starting location if there is a close to that location. The previously inverted result was used for current inversion initialization for the other traces. If no well impedance was available at the location where we expected to start, a random initialization was used for that location. To be compatible with the conventional methods, some fixed points of the impedance which can represent the known stable layer or boundary impedance, can also be used for the inversion. All of these changes improve the results as well as the computational efficiency. The two schemes are tested using a field data set and a synthetic data set. the a priori defined probability distribution function, the previously inverted impedance in the closest trace can be used for starting the current trace inversion. The process can be continued until all traces are inverted. If the starting location we expect has no well impedance or pseudo-velocity, the first trace can be inverted using a random initialization, then the process can be continued as we described above. The entire process can be started in a way such that only in the well location is the initialization replaced by the well impedance or pseudo-velocity for initialization. (2) Fixed (or unchanged) points problem or boundary conditions: Generally, we know a certain layer that is of stable deposition and the impedance is known with a certain precision. To assist the inversion using this information, the points corresponding to the known layers are fixed, i.e., the impedance for these points will not be perturbed. This will reduce the number of perturbations, and will speed up the final determination of the other points which are not corresponding to this layer. However, the algorithm needs to check the points whether they are fixed points or boundaries, and this costs some computational time and slows down the process.