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This paper presents the results of geotechnical and mineralogical investigations on limetreated soft clay soil from Idku City, Egypt, where high organic matters of about 14% exist. Lime was added in the order of 1%, 3%, 5% and 7% by weight and laboratory experiments after 7, 15, 30 and 60 days were conducted including the mineralogical and microstructural examinations, grain size analysis, plasticity limits, unconfined compressive tests, vane shear tests and oedometer tests. The results indicate that soft clay soil of high organic content of 14% can be stabilized satisfactorily with the addition of 7% lime. The results also demonstrate that the changes in the mineralogical contents and soil fabric of high organic lime-treated soft clay improve soil plasticity, strength and compressibility.

Borehole-log data acquisition accounts for a significant proportion of exploration, appraisal and field development costs. As part of Shell technical competitive scoping, there is an ambition to increase formation evaluation value of information by leveraging drilling and mudlogging data, which traditionally often used in petrophysical or reservoir modelling workflow. Often data acquisition and formation evaluation for the shallow hole sections (or overburden) are incomplete. Logging-while-drilling (LWD) and/or wireline log data coverage is restricted to mostly GR, RES and mud log information and the quality of the logs varied depending on the vendor companies or year of the acquisition. In addition, reservoir characterization logs typically covered only the final few thousand feet of the wellbore thus preventing a full quantitative petrophysical, geomechanical, geological correlation and geophysical modelling, which caused limited understanding of overburden sections in the drilled locations and geohazards risls assessment. Use of neural networks (NN) to predict logs is a well-known in Petrophysic discipline and has often used technology since more than last 10 years. However, the NN model seldon utilized the drilling and mudlogging data (due to lack of calibration and inconsistency) and up until now the industry usually used to predict a synthetic log or fill gaps in a log. With the collaboration between Shell and Quantico, the project team develops a plug-in based on a novel artificial intelligence (AI) logs workflow using neural-network to generate synthetic/AI logs from offset wells logs data, drilling and mudlogging data. The AI logs workflow is trialled in Shell Trinidad & Tobago and Gulf of Mexicooffshore fields. The results of this study indicate the neural network model provides data comparable to that from conventional logging tools over the study area. When comparing the resulting synthetic logs with measured logs, the range of variance is within the expected variance of repeat runs of a conventional logging tool. Cross plots of synthetic versus measured logs indicate a high density of points centralized about the one-to-one line, indicating a robust model with no systematic biases. The QLog approach provides several potential benefits. These include a common framework for producing DTC, DTS, NEU and RHOB logs in one pass from a standard set of drilling, LWD and survey parameters. Since this framework ties together drilling, formation evaluation and geophysical data, the artificial intelligence enhances and possibly enables other petrophysical/QI/rock property analysis that including seismic inversion, high resolution logs, log QC/editing, real-time LWD, drilling optimization and others.

Permeability tensor measurements for three different gas shale samples were done using quasi-steady flow technique in specially designed apparatus in which confining pressure, upstream pore pressure, downstream pore pressure and temperature are independently controlled. The initial pressure difference between upstream and downstream changes only after the pressure pulse passes across the whole sample. Using the quasi-steady flow technique gives the ability to measure axial permeability of three different oriented plugs simultaneous at the same pressure temperature conditions. Measured intrinsic permeability anisotropy ratio in gas shale was 25 % in average. The anisotropy ratio remains almost constant with increasing effective pressure, however permeability magnitudes decrease by almost two orders. This reduction in permeability was described by a cubic k- law and explained by preferential flow through pore like-cracks. The anisotropy ratios response suggested a presence of this type of pores both parallel to and perpendicular to bedding which close upon increasing confining pressure. The pore like-crack throats could be small as the test fluid kinetic molecular diameter (e.g. 0.381 nm for natural gas "Methane").

The studied Gas Shale especially the silica-rich gas-shale samples displayed permeability and elasticity anisotropy behavior. These anisotropy behaviors are closely correlated in terms of the symmetry directions which means the elastic anisotropy and permeability anisotropy share the same cause. All samples, regardless of the measurement direction, show a nonlinear reduction in permeability with increase of effective pressure (up to 3 orders of magnitude), with large variations from sample to sample and measurement direction. While the elasticity showed the less sensitivity to the effective pressure change, the velocities increases with increasing the effective pressure. The permeability and the elasticity anisotropy behaviors showed also a good correlation with quartz and clay content.

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