Recent advances in data science and machine learning (ML) have brought the benefits of these technologies closer to the mainstream of petrophysics. ML systems, where decisions and self-checks are made by carefully designed algorithms, in addition to executing typical tasks such as classification and regression, offer efficient and liberating solutions to the modern petrophysicist. The outline of such a system and its application in the form of a multilevel workflow to a 59-well multifield study are presented in this paper. The main objective of the workflow is to identify outliers in bulk density and compressional slowness logs and to reconstruct them using data-driven predictive models. A secondary objective of the project is to predict shear slowness in zones where such data do not exist. The system is fully automated, designed to optimize the use of all available data, and provide uncertainty estimates. It integrates modern concepts for outlier detection, predictive classification, and regression, as well as multidimensional scaling based on inter-well similarity. Benchmarking of ML results against those created by experienced petrophysicists shows that the ML workflow can provide high-quality answers that compare favorably to those produced by human experts. A second validation exercise, that compares acoustic impedance logs computed from ML answers to actual seismic data, provides further evidence for the accuracy of the ML-generated results. The ML system supports the petrophysicist by easing the burden on repetitive and burdensome quality control tasks. The efficiency gains and time savings created can be used for enhanced effective cross-discipline integration, collaboration, and further innovation.

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Extending the life of mature fields is dependant on reducing cost of operations (drilling & completions) and choosing locations for optimized production. Understanding the implications of anisotropy helps in reducing the uncertainty of wellbore stability models and improving seismic calibration. A major difficulty in extending seismic and sonic processing for geomechanical and geophysical applications within anisotropic media is the determination of an anisotropic velocity model. However, mature fields, where multiple wells are drilled at various deviations, represent optimum conditions for acquiring sonic data to determine anisotropy parameters. A case study is presented in the Forties Field, UK where the anisotropy parameters were studied at one well location from full waveform sonic logs, borehole seismic and core analysis. The field wide sonic data was then integrated with the single well results and used for a field wide calibration of the sonic data to account for the effects of anisotropy caused by the shale layering in the overburden. Subsequently, the wellbore stability predictions and seismic interpretation were updated and showed improvement by utilizing the anisotropy results.

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