Partial Least Squares Regression of Oil Sands Processing Variables within Discrete Event Simulation Digital Twin

Abstract

Oil remains a major contributor to global primary energy supply and is, thus, fundamental to the continued functioning of modern society and related industries. Conventional oil and gas reserves are finite and are being depleted at a relatively rapid pace. With alternative fuels and technologies still unable to fill the gap, research and development of unconventional petroleum resources have accelerated markedly in the past 20 years. With some of the largest bitumen deposits in the world, Canada has an active oil mining and refining industry. Bitumen deposits, also called oil sands, are formed in complex geological environments and subject to a host of syn- and post-depositional processes. As a result, some ores are heterogeneous, at both individual reservoir and regional scales, which poses significant problems in terms of extractive processing. Moreover, with increased environmental awareness and enhanced governmental regulations and industry best practices, it is critical for oil sands producers to improve process efficiencies across the spectrum. Discrete event simulation (DES) is a computational paradigm to develop dynamic digital twins, including the interactions of critical variables and processes. In the case of mining systems, the digital twin includes aspects of geological uncertainty. The resulting simulations include alternate operational modes that are characterized by separate operational policies and tactics. The current DES framework has been customized to integrate predictive modelling data, generated via partial least squares (PLS) regression, in order to evaluate system-wide response to geological uncertainty. Sample computations that are based on data from Canada’s oil sands are presented, showing the framework to be a powerful tool to assess and attenuate operational risk factors in the extractive processing of bitumen deposits. Specifically, this work addresses blending control strategies prior to bitumen extraction and provides a pathway to incorporate geological variation into decision-making processes throughout the value chain.

Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning

“…Cell division mainly involves the division of the maternal cell, which divides into two daughter cells. Cell division is generally divided into nuclear and cytoplasmic division [ 15 – 17 ]. The maternal cells transmit genetic material to offspring mainly through nuclear division.…”

Integrated Artificial Neural Network and Discrete Event Simulation Framework for Regional Development of Refractory Gold Systems

“…As previously noted, this integrative approach can be used in the development of simulation-based digital twins. Similar to recent work by Wilson et al [40], the proposed framework is currently intended for offline design and/or early implementation purposes and can thus be termed a "digital model", a subtype of the digital twin paradigm [56,57]. However, it would be possible to integrate the models into a mine control system, whereby the framework could be gradually migrated to an online state.…”

“…Recent work focused on concentrator and smelter dynamics has stressed the importance of coupling alternate modes of operation and mass balance with DES in order to develop a robust framework under changing conditions, or as critical thresholds are crossed [23,37,38]. The versatility of this approach is further underscored by a number of recent DES studies dealing with heap leach processes [24], smelter data acquisition modernization [39], tailings dam retreatment [25], oil sands processing [40], gold extraction reagent control [36], rougher flotation cell dynamics [41] and copper mine-to-smelter strategies [42].…”

“…Similar to recent DES modelling work (e.g., [23,36,40]), this study employs a twomode model with multiple configuration rates to optimize the mass balancing of ore feed types and stabilize plant throughput. Operational buffers, including stockpiling and ore blending practices, are also implemented with corresponding adjustments made following the observed system response to geological uncertainty in the ore feeds.…”

A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector

“…The application of AI for accurate sand production prediction in wells [140] and for shale well production [141] have also shown promising results. The use of discrete event simulation DT in studying the operational risk in oil sand mining and processing of bitumen in response to geological uncertainty was shown to be a good coordination tool [142], enhancement of drilling operations using IoT and AI models [143] and detection of fault in submersible screw pumps in oil wells [144]. The use of AI in the detection of casing damage due to non-uniform in-situ stress has been explored in [145]- [147].…”