Environomics Framework for Sustainable Business Practices: Industrial Case Studies on True Impact Reduction and Process Optimization Through AI

Abstract

Artificial Intelligence (AI) has significant potential to optimize practices, processes, and energy consumption along with maximizing yield, quality, and uptime. This has substantial impact on putting organizations on the path to net-zero, as such optimizations can reduce greenhouse gas emissions by 20% with minimal capital investments. This comprehensive study presents proven industrial case studies that delivered economically strong strategies coupled with sustainability practice and providing strategic insights to identify, manage and/or attenuate the associated impacts. Environomics presented in this study is a novel framework which deals with unifying economic strategies with sustainability practices (through artificial intelligence) for optimal business performance in terms of finances but also environmental impact. This is achieved through a track, trace, and optimize approach for resources (particularly emissions, energy, water, waste, materials,, and safety) This was achieved through a combination of AI methods such as unsupervised machine learning, multi-variate optimization, and the implementation of similarity measures. A few of the inputs included well data (including production data, drilling data, completion data etc.), logistics/supply chain data (scheduling data, production inventory, mobilization data etc.), safety data (near-miss, observations, hazards, disciplines and insights etc.) with associated costs and emission data. Multiple industrial case studies are presented where sustainability metrics are identified through validated AI models to optimize productivity while reducing emissions and inventory. For instance, well profiling can be used to identify historical parameters that have maximized production potential while optimizing for aspects such as cost or emissions. Furthermore, we can identify the optimal completion parameters for a new well which satisfies carbon targets, use well profiles to build an optimized drilling schedule that meets budget or production criteria while still achieving production targets and optimizing drilling rig routes. Thus, the approach can quickly (within run time) solve interrelated environomic challenges in the reservoir studies space and the field development space. Further case studies indicate that the supply chain can have immense optimization impact on scope 3 aspects with results indicating 30-50% asset utilization improvement with respect to fleets (Vessel, Truck, Rigs). With respect to materials, a 10-20% reduction of material inventory levels all improved through AI. As the workforce are also part of the environment it has been observed that identifying unsafe behaviors within a large operation, also leads to enhanced sustainability behaviors. The models indicate potential of overall emission reduction ranging from 12-20%. This led to the comprehensive framework presented in this study to support sustainable practices that are also economically feasible and deployable. The real-time sustainability metrics generated has immense values in terms of decision-making processes and scenario generation in a fraction of the time that is required using traditional approaches. In addition to assessing the scope of impact, a novel multidisciplinary study and framework is presented to analyze environomic strategies to propose a market-oriented approach through the application of artificial intelligence. Furthermore, industrial, and academic case studies have been evaluated to identify, predict, and optimize the crucial parameters within such workflows that are effective in reducing resources utilized and associated emissions.