Well Logging Operations Optimization at Scale Using Closed Loop Data Analytics Workflow
Authors: Torlov Vladislav S. , Alakeely Abdullah A. , Bhat Jeelani G. , Malibari Waiam H. 
Published: October 2023

Abstract
With the increased data collection pertaining to Well Logging Operations in the field, the solution to convert such information to added business value is essential. The paper presents an implementation of a transformative data-driven evaluation workflow for such complex data. The novel methodology represents a closed loop improvement system, where the performances metrics are established and monitored. In the outcome, recorded and potential improvements are quantified and analyzed to reach the optimal levels of execution.
The output of the created application is designed to contain high resolution multivariate temporal record of logging activities and extensive set of relevant metadata (event depth, tensions, location, instrumentation, etc.). Additionally, the time log of downhole formation testing stationary events and survey progress (contamination cleanup progression, pumping rate, volume, etc.) is included. Descriptive analytics and machine learning methods are used to establish operations efficiency metrics for a variety of activities, that are a part of a five-level hierarchical tree containing over 250 distinct events. The model-based metric is periodically reviewed and duration is compared with individual events data.
The set of over 300,000 operations activities and thousands of formation testing stations (pretests, sampling etc.) acquired by several major logging providers are used as a periodically updated reference. In addition to the guidelines, that exhibit a wide range not usable for efficiency assessment, a set of machine learning models (parametric and ensemble trees) are created to rank the job parameters affecting execution efficiency expressed in activity duration. The top predictors are used to create models for performances metrics and to compare distributions of the difference between the calculated and recorded values. This is done by activity type, e.g. formation tester sampling stationary duration estimator employs transforms of mobility, overbalance, pumped volume, inlet type and other predictors. Tripping speeds are shown to be affected by well depth, type of the job etc. The models revealed significant differences in performance distribution between logging vendors and other attributes. Considering the models uncertainty, the improvement opportunities are identified and quantified. Performance metric is periodically updated, eventually leading towards the optimized values that can also be updated as new technologies emerge and affect any of the trackable items. e.g. faster data acquisition, improved tools deployment, cables or accessories that enhance tripping speed, etc.
The presented closed loop evaluation methodology is used to drive logging operations optimization, and sustain the efficiency gains over time. Compared to rudimentary operating vs non-operating time split, the comprehensive system of performance indicators allows by-activity and overall efficiency assessment that carries significantly more insights. The evaluation of stationary events duration for formation testing can be used to quantify efficiency of different instrumentation normalized for downhole settings.