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The wellbore stability is an important issue in the drilling operation. Wellbore instability can interrupt the drilling and waste lots of time and money. The drilling mud is used to keep up the stability of the wellbore. Therefore, selecting the proper mud weight is an important issue in the drilling industry. The goal of this research is presenting an efficient mud weight estimator using deep learning techniques. To obtain this goal, a relatively big dataset (contained more than half-million samples) has been compiled from 116 wells of two fields in the United Kingdom and Norway. Our main contributions are assembling this large dataset, and applying the deep learning techniques to obtain efficient mud weight estimators. Our estimator is an artificial neural network with five hidden layers and 256 nodes in each layer that is able to estimate the mud weight for new wells and depths with the mean absolute error (MAE) of less than ±0.04 pound per gallon (ppg). In various experiments, the presented model has been challenged and the real-world conditions have been simulated. The results have shown that our model can be reliable and efficient in the real world. [ABSTRACT FROM AUTHOR]

The primary aim of this paper is to explore theoretical and practical aspects of the soft system methodology (SSM) and simulation combination as a multi‐paradigm multi‐methodology approach through action research. The important theoretical findings from a systematic literature review show that previously published researches have mainly applied SSM to structure contents of messy problems and for model building. We identified two types of combination designs: (i) simulation enhancement and (ii) mixed methods. To further investigate the practical aspects of combined methodologies, we apply SSM in the conception phase of agent‐based modelling to an Iranian upstream oil and gas company. We show that a combined model offers the following advantages: It (i) improves our understanding of the problem, (ii) reflects upon the modelling process, (iii) provides learning for interveners and (iv) enables the proposition of technically sound, systematically feasible and culturally desirable agreed solutions for improving the situation. [ABSTRACT FROM AUTHOR]