Case Study

Case Study

Pestcovoe Oil & Gas Field Modeling with Wells, 3D Seismic and Potential Fields Data

PANGEA system was installed in TyumenNIIgiprogaz (Tyumen) in February, 1997. It was used for Pestcovoe oil & gas field BU-9/2 reservoir characterization with wells, 3D seismic and potential fields data. 3D seismic data were processed and interpreted in TyumenNIIgiprogaz with LANDMARK PROMAX and Seisworks 3D software. However net pay distribution and fluid flow barriers localization were not successfully determined.

Areal Data Integrator The initial data for the prognosis of the net pay distribution were:
	3D seismic data:
		BU-9/2 top depth map;
		Amplitudes maps corresponding to reflections from BU-9/2 and BU-9/1 layers;
	Gravity local anomalies map;
	Magnetic local anomalies map.
The net pay thickness H_ef in the wells 1, 3, 4, 7, 10, 11, 16, 17, 19, 20, 25, 26, 34, and 36 was used as primary information.
The initial data were continued and converted to obtain transforms, which were used together for an integrated analysis to extract the most informative attributes, and the subset of Multi-D attributes best honoring values of H_ef at the well sites.
Correlation Analysis programs showed that the wells adequately characterized the investigated area in the Multi Dimensional space of attributes, which has been chosen to predict the net pay distribution.
The Clustering program based on fuzzy set recognition allowed to divide all the area into 8 classes, i.e. homogenous groups of points (regular grid sites). Every point characterized the elementary geological body defined by a cell of regular grid and layer properties. The quality of classification was estimated with probability maps (membership functions).
The specific relation between H_ef and Multi-D attributes was built for every class which made possible the best prediction of H_ef with the Contouring program.


The best Multi-D attributes included:
	BU-9/2 top depth map;
	Amplitude map of reflection from BU-9/2 after smoothing and normalization;
	Energy difference of reflected waves from BU-9/2 and BU-9/1 after smoothing and normalization;
	Low frequency component of local magnetic anomalies map.

Profile Multi Dimensional Interpretation The interpretation of the line L-630 with Section Classification module allowed to reveal lithological heterogeneity of reservoir layers.
The investigated interval was divided with Clustering program into 4 clusters by the combination of seismic attributes, instantaneous amplitudes, frequencies, phases and coherently filtered section. These four clusters corresponded to collectors with gas, oil, water and surrounding rocks. The reliability of classification was up to 90% for gas and surrounding rocks.
During the combined analysis of faults and fractures areal scheme obtained from magnetic data (ADI Transform programs) and the results of the interpretation of the seismic sections, was noticed the coincidence of the faults and zones of degradation of BU-9/2 reservoir, obtained with 3D seismic and potential fields data. This is evident from the figure, where the fragment of areal scheme of faults along the line L-630 and seismic classified section are represented.


*Conclusion (quoted by GAZPROM)*
"The application of the PANGEA's system for the prognosis of the oil & gas saturated intervals of the layer BU-9/2 at the Pestcovoe oil field allowed to:

	Estimate the position of gas-oil and oil-water contacts by Clustering program;
	To localize the main disturbances of reservoir properties;
	To carry out automatically the forecast of H_ef.
The reliability of the forecast was confirmed by the data of wells 551 and 208.

Well	Prognosis (m)	Test (m)
551	6,8 (oil)	11,2 (gas)
208	6,2 (oil)	10,0 (gas)

The new data made possible to improve the details of the map of H_ef in the monitoring mode. Additional information resulted only in subtle variation of the map. That proved the correctness of the forecast. The works have confirmed the high efficiency of the PANGEA system when used as a tool to acquire additional geological information from Multi-Dimensional analysis of diverse G & G data."

See other Case Studies

| Back | Home | Next |