Laurence R. Lines

Smiles and frowns in migration/velocity analysis

Reliable seismic depth migrations require an accurate input velocity model. Inaccurate velocity estimates will distort point diffractors into smiles or frowns on a depth section. For both poststack and prestack migrated sections, high velocities cause deep smiles while low velocities cause shallow frowns on migrated gathers. However, for prestack images in the offset domain, high velocities cause deep frowns while low velocities cause shallow smiles. If the velocity is correct, there will be no variation in the depth migration as a function of offset and no smiles or frowns in the offset domain. We explain migration responses both mathematically and graphically and thereby provide the basis for depth migration velocity analysis.

Linear inverse theory and deconvolution

Seismic source wavelet deconvolution can be treated within the framework of the Backus‐Gilbert (BG) inverse theory. A time shift‐invariant version of this theory leads to the Wiener shaping filter, which has enjoyed widespread use for source wavelet deconvolution in exploration seismology. The model of the BG theory is the ground impulse response, the BG mapping kernel is the source wavelet, and the BG resolving kernel is the convolution between the source wavelet and the Wiener shaping filter. BG inversion involves the minimization of an optimality criterion under a set of constraints. The application of the BG “filter energy” or “noise output power” constraint to Wiener filter design leads to the familiar prewhitening parameter that stabilizes the filter on the one hand, but degrades resolution on the other. The BG “unimodular” constraint produces an unbiased estimate of the model, or ground impulse response. These constraints provide novel insights into the performance of deconvolution filters.

Three Term AVO Waveform Inversion

Summary Density reflectivity is a useful AVO attribute to infer fluid saturation. However, accurate density reflectivity estimates are difficult to obtain due to the ill-conditioned nature of the inverse problem. A small amount of noise will lead to large errors in the estimates. To improve the stability of the inversion, large angles and offsets are required, but these bring their own problems. NMO stretch and offset dependent tuning are two of these. This paper develops and demonstrates an AVO waveform inversion that incorporates into its forward model these factors allowing for accurate estimates even in their presence. Well constraints and various regularization strategies are employed to further enhance the reliability of the solution.

Constrained three-parameter AVO inversion and uncertainty analysis

SUMMARY Bayes’ theorem is used to derive a three-parameter non-linear AVO inversion. Geologic constraints based on available well-control or rock-physical relationships are incorporated to help stabilize the solution. Parameter uncertainty estimates arise naturally as part of the derivation and provide estimates of the reliability of the different parameters. The resulting parameter and uncertainty estimates may be transformed to a variety of elastic and rock-physical AVO attributes popular in the literature using a transform matrix.

Heavy-oil reservoir characterization using elastic wave properties

This case history describes seismic monitoring efforts at Pikes Peak Field, a prolific heavy-oil field (production exceeding 42 million barrels) just east of the Alberta-Saskatchewan border that has been operated by Husky Energy since 1981.

Prestack depth migration of an Alberta Foothills data set—The Husky experience

We produce depth images for an Alberta Foothills line by iteratively using a number of migration and velocity analysis techniques. In imaging steeply dipping layers of a foothills data set, it is apparent that thrust belt geology can violate the conventional assumptions of elevation datum corrections and common midpoint (CMP) stacking. To circumvent these problems, we use migration from topography in which we perform prestack depth migration on the data using correct source and receiver elevations. Migration from topography produces enhanced images of steep shallow reflectors when compared to conventional processing. In addition to migration from topography, we couple prestack depth migration with the continuous adjustment of velocity depth models. A number of criteria are used in doing this. These criteria require that our velocity estimates produce a focused image and that migrated depths in common image gathers be independent of source-receiver offset. Velocity models are estimated by a series of iterative and interpretive steps involving prestack migration velocity analysis and structural interpretation. Overlays of velocity models on depth migrations should generally show consistency between velocity boundaries and reflection depths. Our preferred seismic depth section has been produced by using prestack reverse-time depth migration coupled with careful geological interpretation.

Sensitivity analysis of seismic depth migrations

Depth migration is the processing step that can position seismic reflectors in their proper spatial locations beneath the earth9s surface. Accurate seismic imaging assists in structural interpretation and determination of optimum drilling locations. There are many migration algorithms to perform this depth imaging step. Many of these algorithms and their applications to foothills data have been summarized by Lines et al. (1999). As mentioned by Parkes and Hatton (1987), most depth migration algorithms and seismic time measurements are quite accurate, but to improve the migrated image, greater emphasis needs to be placed on improving the accuracy of the input velocity field. In addition, a greater awareness of depth imaging errors due to velocity inaccuracy is crucial. Parkes and Hatton (1987) state that depth migration uncertainty is primarily a result of the velocity field uncertainty used to map seismic data from time to space.

Applications of OBC recording

In recent years, there has been much interest in seismic imaging within the marine environment via four-component (4-C) ocean-bottom-cable (OBC) recording. The 4-C OBC sensor is equipped with a single hydrophone (pressure detector) plus a three-component (3-C) geophone (particle velocity detector). The 3-C geophone records the full three-dimensional ground motion via one vertical component and two orthogonal horizontal components. 4-C OBC recording has several advantages over conventional towed-streamer technology.

Integration of seismic methods with reservoir simulation, Pikes Peak Heavy Oil Field, Saskatchewan

Reservoir characterization is essential for providing optimal recovery from heavy-oil fields. The process of reservoir characterization is demonstrated for a steam injection project at Pikes Peak heavy oil field near Lloydminster, Saskatchewan, Canada. Geologic, geophysical and reservoir engineering data are used to improve the interpretation of reservoir conditions. There is ambiguity in modeling any of these data. However, in modeling all data sets in “cooperative inversion,” ambiguity is decreased, thereby enhancing our knowledge of the reservoir. One of the main benefits of this oil-field modeling is that bypassed oil and steam fronts are effectively mapped.

Application of the Radial Basis Function Neural Network to the Prediction of Log Properties From Seismic Attributes - A Channel Sand Case Study

In this paper, we use the radial basis function neural network, or RBFN, to predict reservoir log properties from seismic attributes. We also compare the results of this approach with the use of the generalized regression neural network, GRNN, for the same problem, as proposed by Hampson et al (2001). We discuss both the theory behind these methods and the methodology involved in applying neural networks to seismic attributes. We then illustrate the method using the Blackfoot 3D seismic volume, a channel sand example from Alberta.