Borehole images play a crucial role in tight gas exploration. Recognition of sedimentary features helps in understanding the depositional architecture and allows refinement of the facies model for flow unit identification and stimulation treatment. The structural analysis of faults and fractures provides clues not only about the tectonic history, but also about the possible conduits for fluid migration that lead to diagenesis. The diagenetic imprints and their impact in a sequence stratigraphic framework can be understood through textural analysis performed on the borehole images across the field. And, hydrofracturing of tight gas reservoirs require important input from the borehole images in understanding the variability of stress regimes.

Established micro-resistivity imagers for the water-base mud (WBM) environment provide robust results, except when there is a large contrast between the formation resistivity (Rt) and the mud resistivity (Rm). With more frequent use of hyper-saline mud, a new and improved definition imager is deployed to obtain high quality images. Novel hardware and improved signal processing algorithms are employed to acquire the images despite the hostile conditions provided by the combination of lowresistivity salt-saturated (WBM) and high formation resistivity that would otherwise impede the data quality. Early field testing of this enhanced capability took place in the Sultanate of Oman and the examples of its improved performance are presented.

Getting the most detailed image data in oil-base mud (OBM) is challenging compared to the WBM systems. As an alternative to the options commercially available in the industry today, the new high-definition imager developed for the WBM system can also be used under favorable conditions to acquire valid images in the OBM. The high-definition imager works best in OBM when both formation resistivity and mud permittivity are high.

A workflow is developed for the Go - No Go decisions for borehole imaging tools in different mud systems for tight gas reservoirs. It is important at the planning phase of the logging programs to anticipate the imaging tool behavior in the proposed mud system and conditions. The results from trials made in the tight gas reservoirs of North Oman provided the basis for a decision tree for imaging, since the logging environment exerts a strong control on data quality. The decision-tree presented here aims to ensure that images acquired are the most suitable for detailed geologic interpretation and subsequent integration in development plans for optimal exploitation of tight gas sands in Oman.