Lars Sonneland

New Paradigm of Fault Interpretation

A high-level fault interpretation workflow using automatically extracted surfaces is presented with special attention on the human interaction part. The first step of the workflow is to generate a fault attribute that enhances the discontinuities in the seismic data. Fault-like surfaces are then extracted using an algorithm called “Ant Tracking”. The surfaces are then loaded into a 3D analysis tool where the interpreter, by interactively verifying, combining and deleting surfaces, decides what is to be the final interpretation. The interpreter works on two levels in the analysis tool; on the fault system level, and the individual surface level. This “top-down” approach to fault interpretation is demonstrated by a case study of two fields offshore mid Norway.

Automated salt body extraction from seismic data using the level set method

A solution for automated salt body extraction from seismic data has been developed. The solution is based on a level set algorithm, used in combination with seismic attributes that discriminate between salt and sediments and highlight top and base salt reflections and terminations of sediments against salt. In an automated process, steps to obtain the gross volume of salt are combined with local adjustments to place the salt boundary at the appropriate attribute extrema. The process also includes a local stop criterion based on a salt boundary confidence measure. The ability to separate completed salt boundary parts from uncertain regions where the salt boundary has not been identified enables the interpreter to focus on the parts where manual interpretation is required. User constraints, including manual interpretations, are honoured by the algorithm. The methodology is demonstrated on a multi-client seismic data set from the Gulf of Mexico.

Construction of Reservoir Maps From Seismic Classifier Maps

In a recent paper we introduced the seismic classifier concept. We have demonstrated how classifiers can help us to identify particular facets of the subsurface “architecture” and how they combine the structural interpretation and the 3D seismic data in a very compact way. Through classifiers like reflection intensity, acoustic impedance contrast and reflection hetereogenity indicator the interpreter can in a quantitative sense discriminate between properties relevant for his particular problem. We will extend our classifier-family further and point out how the “resolution” in our data-set effect the construction of these new classifier maps. Interpretations based on classifier-maps require new functionality in the application software. Based on examples from case studies we will identify these requirements and apply them. Of particular relevance is when the projections of the geological model onto the classifier map can be revealed. Then these maps can indicate the paleogeographical scene. Finally we will present some results from a case-study where we constructed qualitative approximation to the distribution map of hydrocarbon volume in-place. Introduction Our dataset for this example is from High Island, a gasprovince offshore Texas. The scenario as pointed out in [2] is: A 3D survey was shot and processed over the province. The areal extent of the survey was 25.000 m* and the grid-size 25 x 25 m*. There were two wells in the area, but we had access only to a very limited part of the well-information. Following is a short introduction to stratigraphic settings of the province. Stratigraphy The South Addition of High Island lies along the western margin of a large, productive, plio-Pleistocene depocenter situated offshore of Texas. The sediments in the study area represent the latest in a serie of progradational wedges developed in the tertiary and quarternary. These wedges are comprised mainly of fluvial and detaic sandshale sequences deposited in the outer and upper shelf environments. The productive sand of interest is thought to be the result of deltaic depositional processes. Well A penetrated the top of the sand unit at 996 m and the gas-water contact at 1033 m. The sand is 59 m thick with thin inter-bedded shale layers. Well B penetrated the top of the unit at 1033 m and saw 49 m of sand. Porosity ranges from 20-25% at this level with a gas saturation of 48% at the Well A location. Fluid contact maps The objective is to map the contacts of the fluid boundaries in the reservoir. From our Well A we know we have only gas and water present in the reservoir so our objective will be to produce the gas-water-contact (GWC) maps for different the reservoir compartments. To arrive at this result we will make two assumptions: That the reservoir is in a gravity controlled equilibrium state, and that the intensity level or bright-spot effect is the same aa we established in the calibration point. The procedure is: Given the structural map of the Trim A level (top og sand) t, = f(z,y) we generate the intensity attribute map (refer PI): ri+Az AE (ZtYGi) = J 1 R (ZjY,zi) 1 &

Interactive seismic processing

The high speed of super-computers has made possible the short turn-around-time required for interactive data-processing. Advances in the quality of graphic display devices and higher datatransmission speeds have meant new possibilities for data acquisition and processing QC and for workstation based interpretation systems. We will give an overview of the uses of interactive processing systems in a production environment and of their integration with existing interpretation systems. The examples will include parameter testing filter design 3D velocity analysis iterative depth migration Emphasis will be put on “interpretation driven” processing and how this could be faciliated in a workstation environment. A beneficial side effect of the use of super-computers has been a growing portability of seismic processing software so that in some cases identical software can be used on stand-alone microcomputers or on micros integrated with interpretation systems. This portability is due mainly to the absence of attached arrayprocessors.