Mustafa Al-Ali

Vibrator attribute leading velocity estimation

Accurate knowledge of the near-surface velocity macromodel is vital for hydrocarbon exploration and reservoir characterization that utilize seismic data because this model is crucial for statics and depthing. However, determination of near-surface velocities using direct measurements via uphole surveys is economically prohibitive. Consequently, geophysicists face a problem when determining how to interpolate between sparse near-surface well control when near-surface velocity varies laterally with lithology that, in turn, does not vary equally in all directions. Therefore, techniques are needed to estimate near-surface velocity using other available data in order to minimize the associated risk resulting from incomplete knowledge of the near-surface velocity model.

Development of near surface models in Saudi Arabia for low relief structures and complex near surface geology

In the past the geologic information of the near surface in Saudi Arabia was not as critical an issue due to the fact that the prospective geologic structures tended to be much larger than the near surface statics variation. Today, the majority of the prospects are lower relief structures. The static corrections in the near surface statics can include medium to long wavelength statics up to +/80 ms in most prospective areas in Saudi Arabia. Several approaches have been developed to resolve these near surface issues and to better image these low relief structures. These methods are the frozen model, layer modeling and geostatistics. Using these various methods the static uncertainty is reduced in the near surface which in turn gives a more accurate sub-surface image.

Assessing estimated velocity-depth models: Finding error bars in tomographic inversion

In conventional migration velocity analysis methods, a velocity model is estimated that results in flattened events in common-image gathers. However, after this process, no information is available on the accuracy of this velocity model. A statistical analysis of velocity-model parameters is very difficult because of the integrated nature of the process. In common-focus-point technology, velocity estimation is split into two processes: a first step to estimate one-way focusing operators from the seismic data and a second step to translate these one-way propagation operators into a velocity-depth model. Because the second step does not involve seismic data and uses a hands-off model parameterization, a statistical analysis of the inversion result becomes rather straightforward. We developed a methodology for obtaining a suite of possible solutions, from which statistical measures can be extracted. By varying initial settings, the inversion of one-way traveltimes provides a space of solutions. Rather than having a single estimated model, we can obtain an ensemble of models. By performing statistical analysis of this ensemble, the error bars of the estimated velocity model can be retrieved. The procedure was tested for a 2D synthetic and field data set, for which the latter compares favorably to a conventional two-way traveltime tomography approach. The information provided by such an analysis is important because it shows the reliability of the final estimated model and could provide feedback for acquisition geometry design. More or better data might be needed to obtain a model to which a smaller degree of ambiguity is associated.

Conventional and sparse 3D: What is in between?

A chief consideration when exploring for deep subtle structural traps in areas characterized by long and medium wavelength statics is shallow chronostratigraphic markers used for flattening or isochroning in interpretation. Failure to image these markers is intrinsic in sparse 3D survey designs aimed for imaging deep targets. Furthermore, the relatively low fold of sparse 3D data, especially at shallower horizons, makes initial processing steps like velocity analysis and statics correction difficult. This paper describes a sparse 3D acquisition design developed to address these issues by introducing additional source points along the receiver lines. Data examples will be shown illustrating the advantages of the method.

Common-focus point-based target-oriented imaging approach for continuous seismic reservoir monitoring

The ideal approach for continuous reservoir monitoring should allow generation of fast and accurate images to cope with the massive datasets acquired for such task. Conventionally, rigorous depth-oriented velocity estimation methods should be performed to produce sufficiently accurate velocity models. Being different from the traditional way, the target-oriented imaging technology based on Common Focus Point (CFP) theory could be an alternative for continuous reservoir monitoring. The solution is based on a robust data driven iterative operator updating strategy without deriving a detailed velocity model. The same focusing operator is applied on successive 3D seismic datasets for the first time to generate efficient and accurate 4D target-oriented seismic stacked images from time-lapse field seismic datasets acquired in a CO2 injection project in Saudi Arabia. Using the focusing operator, the target oriented prestack angle domain common image gathers (ADCIG) could be derived to perform amplitude versus an...