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In this work, we present and apply a comprehensive prescriptive analytics framework for well completion optimization in unconventional resources. The methodology involves 1) data processing, ingestion into databases, and data cleansing 2) application of AutoML for generating an accurate machine learning model, and 3) numerical optimization of decision parameters for minimizing an economic objective. Input parameters to the machine learning model include system parameters (such as well location and trajectory, existence and type of artificial lift) and decision parameters (such as number of stages, amount of stimulation material). The AutoML process automatically searches among various machine-learning algorithms (e.g., neural networks, random forest), to find the best algorithm and the best associated hyper-parameters. In the third step, a multi-objective optimization process is implemented to simultaneously optimize the 12-month oil producton (Qoil) and the completion cost divided by Qoil. The methodology is applied to a real case in the Permian Basin, in collaboration with Diamondback Energy. The results demonstrate that the two objectives are conflicting. The construction of the Pareto front, which shows a set of optimal solutions, provides a visual trade-off for decision makers and enables them to select a strategy that minimizes the cost, but does not sacrifice too much of the 12-month oil production.

Despite being the most widely used artificial lift method for high-producing oil wells, ESPs still experience unplanned failures that impact well productivity and overall field economics. Our advanced ESP Predictive Failure Analytics (PFA) can help detect ESP events ahead of time and extend the overall ESP run life. PFA enabled a major Latin American operator, experiencing frequent unplanned ESP failures, to identify critical events while pumps were running and take remedial actions to extend ESPs run life. Methods, Procedures, Process PFA leverages artificial intelligence (AI), statistical and physics-based models to reliably predict Remaining Useful Life (RUL) and possible failure cause. The models are trained using historical sensor time-series from both running and failed ESPs. The trained models are deployed to predict short-term events that may lead to immediate failure, such as a broken shaft, short-circuit, grounded downhole failure; as well as long-term events which build up over time, such as pump low efficiency, sand, scale deposition and gassy conditions. Results, Observations, Conclusions For this study, we used two ESPs. For ESP-1, PFA predicted broken shaft/missed pump stages after a sudden decline in motor current and production rate. As the production rate declined beyond the minimum recommended operating range, PFA identified downthrust condition and estimated a significant RUL reduction. PFA enables the operator to quickly schedule a workover, reducing downtime. For ESP-2, intake pressure and motor current started decreasing and motor temperature started increasing. PFA predicted sand influx and estimated a significant RUL reduction. A chemical injection was applied to reduce sand, and avoid an imminent failure leading to PFA estimating an increased RUL. Novel/Additive Information PFA is an innovative approach which combines AI, statistical and physics-based methods to provide explainable predictions of ESP failure. Unlike commonly used threshold-based approaches, PFA tends to generate fewer alarms which enables proactive optimization of ESP performance, avoiding unplanned failures and extend ESP run life.

Planetary Defense is an important and challenging topic, and long‐term mitigation of asteroid impacts have been discussed by governments and space agencies; however, short‐term solutions of comet impacts are rarely considered. 34 participants from 17 countries tackled this problem, a comet impact two years after detection, at the 2015‐Space Studies Program and produced the Roadmap for EArth Defense Initiatives (READI). The Integrated Product and Process Development (IPPD) methodology was used to identify the mission statement, define requirements and needs, and provide possible solutions. This paper concentrates on how the IPPD was applied as a systems approach to each phase of our project, where the whole system was Planetary Defense, and its architectural decomposition was divided into five key elements: detection and tracking, deflection techniques, global collaboration, outreach and education, and evacuation and recovery. The importance of applying human factor concepts throughout the project is addressed through challenges faced, solutions applied, and lessons learned. Verification and validation of selected options are presented, along with the use of various project management tools. Furthermore, we discuss our results and recommendations for Planetary Defense, as they are products of our analysis using the IPPD approach to systems engineering.