--> ABSTRACT: Inferring Permeability Attributes of a Fault from Multiple Types of Temporal and Spatial Hydraulic Data: A Case Study, by Johnson, Brann; #90026 (2004)

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Johnson, Brann1 
(1) Texas A&M University, College Station, TX

ABSTRACT: Inferring Permeability Attributes of a Fault from Multiple Types of Temporal and Spatial Hydraulic Data: A Case Study

A fault-partitioned, groundwater aquifer/aquitard system in central Texas is an excellent analog for fault compartmentalized, siliciclastic, petroleum reservoirs and is providing a cost-effective, field laboratory for study of fault structure and fault-rock permeability and spatial attributes. Current focus is on a dip-slip dominant fault with 20 m of displacement that partially offsets the aquifer/aquitard system and impedes cross-fault flow. Continuous core and geophysical logs from eleven, closely-spaced boreholes allowed construction of a high-resolution geological model. Hydraulic data are obtained from open-hole wells and multilevel monitoring systems installed in eight boreholes that provide 94, pressure measurement zones. Three types of hydraulic data are being measured and analyzed using numerical, forward and inverse modeling: (1) quasi-steady state head distribution, (2) data from full-reservoir pump tests, and (3) localized, cross-well, multilevel, slug interference tests. 
Analysis of the first two data types requires modeling the flow field in a large region of the reservoir. Simplistic local interpretation of head distributions or transient histories must be done with caution. For example, the quasi-steady state head distribution reveals a ten-fold variation of head change across the fault, but the inverse model infers a five order of magnitude (30 - 0.0003 md) spatial variation of fault-rock permeability. The multiwell, multilevel localized slug tests are proving to be especially informative. The associated flow field is localized, which allows useful qualitative inferences in advance of numerical analysis and simplifies the model space considered. To provide insight, examples of how faults are reflected in the three data types are presented.

 

AAPG Search and Discovery Article #90026©2004 AAPG Annual Meeting, Dallas, Texas, April 18-21, 2004.