Integrated Interpretation Details

BASIC INTERPRETATION
Raw seismic records are infested with many types of random and coherent noises. Different noise elimination routines are used to effectively remove these noises – resulting in effective signal processing, signal to noise ratio improvement and better imaging.

Time slice from a processed 3D volume provides a quick look of the subsurface structurization (A). In the present example, the abrupt termination of events marks the basin margin fault beyond which basement is encountered and no sediment is deposited. An interpreted time horizon is displayed as a time structure map with faults after appropriate gridding and contouring (B). The corresponding depth structure map (D) is generated from the time structure map with the appropriate average velocity field (C) over the time horizon. The average velocity field is generated calibrating Seismic derived velocity field with well velocities and distributing the error over the area.

2D Seismic Interpretation in Complex Geological Area of Afghanistan: Reconstructed interpreted line showing Salt dome areas

RMS Amplitude Attribute on 2D line showing bright amplitude indicate possible reservoir facies

LOG/PETROPHYSICAL ANALYSIS
Integration of well log data and analysis is crucial for Integrated interpretation. Log correlation (A) is mandatory for well top identification at various stratigraphic levels for well to seismic tie. Log processing ( B) provides important information of Sand/Shale content, porosity information and saturation information. Petro-physical analysis (C) and (D) extends further insight into the depositional environment, generation potential etc. These information are crucial input for reservoir characterization and prospect evaluation.

Multi-Attribute Classification
Based on Fuzzy consideration and Membership Function using different input attributes along a stratal surface. Membership functions are constructed analysing the statistical properties of the attributes and response at the known well locations. Multi-attribute classifications attempts to capture common/related features between the attributes supervised by the well location attributes.

Non Parametric Regression Analysis
Based on ACE ( Alternate Conditional Expectation) concept, attempts to establish complex hidden relationship between seismic properties with log property. The novelty of this method lies in the fact that it does not require an a-priory model. The input – target variable relationship is rather established from the data analysis itself. In this present example, Porosity is predicted from seismic attributes of Impedance and Reflection Strength (A). The excellent match at well location is noteworthy. The derived complex relationship is then applied at map level to generate porosity map (D) from input attribute maps (B) – Impedance and (C) – Reflection Strength respectively.

Simulation with Collocated Co-Kriging
In the present example Collocated CoKriging is used to estimate Gamma Ray values at grid locations based on the sparse GR values at well locations and dense Impedance values available at well locations as well as at each grid locations of a 3D area. Simulation generates different but equi-probable GR maps in its each realization depending on its random computational path through the entire area. From the large no of realizations , statistical outcome like Lower Limit (A), Upper Limit (B), Mean (C) and Standard Deviation (D) maps are generated. Simulation provides a measure of uncertainty in the statistical estimation processes. There is 68% probability that at a given location, the GR value would be between the Lower Limit and Upper Limit of estimation.


Pore Pressure Prediction

Knowledge and information about subsurface pore pressure is critical for designing and drilling trouble-free wells at lowest cost.
Interval velocity variation along depth is related to effective stress and when pore pressure is hydrostatic, this curve is called a normal compaction curve
Primary input data for Pore pressure analysis is the RMS velocity generated during seismic data processing. This input RMS velocity is used for computation of interval velocity and also other different intermediate outputs like Overburden Gradient, Compaction trend etc. in process of Pore pressure analysis