Artun, Emre 1, Mohaghegh, Shahab D. 1, Toro, Jaime 1, Wilson, Tom 1, and Sanchez, Alejandro 2
1West Virginia University
2Anadarko Petroleum Corporation
In the petroleum exploration work flow, geologists and geophysicists use seismic data to forecast the possible existence of hydrocarbon resources by structural mapping of the subsurface, and making interpretations of the reservoir’s facies distribution. Engineers use this information to make decisions on possible locations for new exploration or development wells. The relatively low resolution of seismic data usually limits its further use. Yet, its areal coverage and availability suggest that it has the potential of providing valuable data for more detailed reservoir characterization studies through the process of seismic inversion.
In this study, a novel intelligent seismic inversion methodology is presented to achieve a desirable correlation between relatively low-frequency seismic signals, and the much higher frequency wireline-log data. Vertical seismic profile (VSP) is used as an intermediate step between the well logs and the surface seismic. A synthetic seismic model is developed by using real data and seismic interpretation. In the example presented here, the model represents the Atoka and Morrow formations, and the overlying Pennsylvanian sequence of the Buffalo Valley Field in New Mexico. Artificial neural networks are used to build two independent correlation models between; 1) Surface seismic and VSP, 2) VSP and well logs. After generating virtual VSP’s from the surface seismic, well logs are predicted by using the correlation between VSP and well logs. Density logs were predicted with 87% accuracy through the seismic line. The same procedure can be applied to a complete 3D seismic block to obtain a detailed view of reservoir quality distribution.