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ClinicalTrials.gov (NCT02067065).

This work describes a new methodology based on 12 steps for integrated decision analysis related to petroleum fields development and management considering reservoir simulation, risk analysis, history matching, uncertainty reduction techniques, representative models and selection of production strategy under uncertainty. The example of application is the field UNISIM-I-D which is a benchmark case based on Namorado field, Campos Basin, in Brazil. The main focus of the results is to show that the method can be used in practical applications, i.e., complex reservoirs in different field stages (development and management) because it allows the integration of static (geostatistical images generated by reservoir information) and dynamic data (well production and pressure) to reduce uncertainties allowing risk analysis integrating geological, economic and other uncertainties yielding a decision analysis based on risk-return techniques. In this methodology, no proxy model is used so reservoir simulation is used directly to reproduce field performance. We also show that the methodology is efficient and easy to use, even in complex cases where the computational time is an important concern and in real time operations.

History matching is the process of modifying the uncertain attributes of a reservoir model to reproduce the real reservoir performance. It is a classical reservoir engineering problem and plays an important role in reservoir management since the resulting models are used to support decisions in other tasks such as economic analysis and production strategy. This work introduces a dynamic decision-making optimization framework for history matching problems in which new models are generated based on, and guided by, the dynamic analysis of the data of available solutions. The optimization framework follows a ‘learning-from-data’ approach, and includes two optimizer components that use machine learning techniques, such as unsupervised learning and statistical analysis, to uncover patterns of input attributes that lead to good output responses. These patterns are used to support the decision-making process while generating new, and better, history matched solutions. The proposed framework is applied to a benchmark model (UNISIM-I-H) based on the Namorado field in Brazil. Results show the potential the dynamic decision-making optimization framework has for improving the quality of history matching solutions using a substantial smaller number of simulations when compared with a previous work on the same benchmark.

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Benign biliary strictures comprise a heterogeneous group of diseases. The most common strictures amenable to endoscopic treatment are post-cholecystectomy, post-liver transplantation, related to primary sclerosing cholangitis and to chronic pancreatitis. Endoscopic treatment of benign biliary strictures is widely used as first line therapy, since it is effective, safe, noninvasive and repeatable. Endoscopic techniques currently used are dilation, multiple plastic stents insertion and fully covered self-expandable metal stents. The main indication for dilation alone is primary sclerosing cholangitis related strictures. In the vast majority of the remaining cases, temporary placement of multiple plastic stents with/without dilation is considered the treatment of choice. Although this approach is effective, it requires multiple endoscopic sessions due to the short duration of stent patency. Fully covered self-expandable metal stents appear as a good alternative to plastic stents, since they have an increased radial diameter, longer stent patency, easier insertion technique and similar efficacy. Recent advances in endoscopic technique and various devices have allowed successful treatment in most cases. The development of novel endoscopic techniques and devices is still ongoing.

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