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Drilling fluids with subtle filtration and rheological characteristics are essential to optimize the functioning of oil and gas well drills. The Early Miocene Murree Formation in the Kohat Basin of Pakistan is generally referred to as the Murree Clays. When mixed with water-base drilling mud, ultra-fine particles of the Murree Formation tend to eradicate default fluid rheological properties and result in wellbore instabilities during drilling in that area. To obtain the optimized mud to deliver the drilling operation efficiently, we aimed to mitigate the impact of Murree clay on the drilling mud. We analyzed the drilling mud to observe the effects of the mud additive on clays on the basis of the samples from the Murree Clays. On the basis of the experimental tests, we observed that the rheological properties of mud significantly improved in the presence of KCl. KCl prevented the smectite group swelling inclinations and reduced rheological values to 25%, 33.3%, 48.6%, and 65.2%. The plastic viscosity increased as the concentration of clays increased; however, there was a noticeable reduction in the yield point values with the introduction of KCl. The laboratory results showed that Mud + 4% Clay + 1% KCl proved to be the best mitigation while preserving the rheological and performance characteristics of the mud. Tests enabled the scope to increase the inhibition efficiency and optimize customization. Depending on the clay present in the Murree formation, drilling fluid optimization is proposed to reduce mud-related drilling problems in this area.

Abstract3D seismic data, well logs, core-based lithofacies and photographs have been combined to interpret and model the depositional facies of the Mangahewa Formation of the Maui Gas Field, Taranaki Basin, New Zealand. The primary objective of the study is to generate a robust facies model for the Middle to Late Eocene (47-37 Ma) Mangahewa Formation of the field. The facies model has included eighteen depositional facies spatially distributed over the gas field. These facies are further subgrouped into three broad depositional facies associations, namely marginal marine, shallow marine and offshore environment. We have identified that marginal marine is the most dominant facies association (64%) within the model. The model visualizes estuarine and shoreface sand geobodies dominating over other facies within the model. Both geobodies comprise over 40% of all the facies interpreted in the field. The entire modeling process involves a novel stochastic approach using unique workflow that follows 3D gridding, coding of the facies classes and multiple iterations over the interpreted facies. The model therefore realistically visualizes potential facies responsible for "good"-quality reservoir sands in the Mangahewa Formation with possible retrogradation from older to younger succession.

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