An MINLP formulation for integrating the operational management of crude oil supply

Assis, Leonardo Salsano de; Camponogara, Eduardo; Menezes, Brenno C.; Grossmann, Ignacio E.

doi:10.1016/j.compchemeng.2018.12.014

Cited by 17 publications

(5 citation statements)

References 32 publications

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“…Another study [33] explores the uncertainties relevant to the maritime inventory-routing context, such as weather conditions, vessel reliability, and congestion at storage depots, to ultimately develop robust schedules. Considering the petroleum inventory-routing context, [34] takes into account the different properties of the petroleum produced at different locations and the nonlinear effect on these properties when different crudes are blended. The authors develop a mixed-integer nonlinear programming (MINLP) model, which is solved by means of a decomposition algorithm.…”

Section: Literature On Inventory-routing Problemsmentioning

confidence: 99%

Energy Efficiency in Petroleum Supply Chain Optimization: Push Segment Coordination

Redutskiy,

Balycheva

2024

Energies

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Today, the world is transitioning from traditional energy to clean, renewable sources. The petroleum sector is to play a role in this transition by supporting material and energy needs related to developing new energy systems. It is, therefore, vital that in upcoming years, the petroleum sector runs in a smart and efficient way, which can be achieved by coordination and the meaningful integration of decision-making issues in petroleum supply chains (PSCs). The existing literature on PSC optimization reveals a research gap; specifically, there is an insufficient level of technological detail considered while planning capacities of new infrastructures and its impact on the efficiency of further operations, specifically in the push segment of the PSC. This paper proposes a mixed-integer nonlinear programming model for planning capacities and coordinating activities within the mentioned PSC segment. The infrastructure capacity planning model covers technological details such as hydraulics and pump systems’ operational efficiency. The results reveal that the proposed model and its technological decision-making criterion of minimizing energy consumption drive infrastructural choices and operational modes to achieve machinery performance close to the best efficiency point. Also, the computational results demonstrate how traditional (minimum-cost) approaches lead to inefficient energy use while producing and transporting hydrocarbons. The proposed framework aims to facilitate the preliminary design stage of projects undertaken by engineering contractors in the energy sector.

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Section: Literature On Inventory-routing Problemsmentioning

confidence: 99%

Energy Efficiency in Petroleum Supply Chain Optimization: Push Segment Coordination

Redutskiy,

Balycheva

2024

Energies

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“…Integrating data science can avoid NP-hardness, and is the best strategy, when possible, to avoid creating nonlinear constraints, since MINLP solutions approaches are complex and prone to terminate at a local solution that can be far from the optimum. Assis et al (2019) provided an MINLP formulation for operational management of crude oil supply with FPSOs operation, but topside constraints are not considered neither topside decision-making involving heat and mass transfer, nor electricity-use derived support decision. Their work considers maritime inventory routing of oil crude supply accounting for global variables on the production side since they intended to plan global production with either deterministic or optimistic scenario.…”

Section: State Of the Art And Reviewmentioning

confidence: 99%

Novel Topside and Subsea Production Optimization, Operational Scheduling, and a Generic Plantwide MPC for Process Systems Control and Production Maximization: Oil, Gas, Water and Utilities Decision-Making with Offloading to Supply Chains and Sustainability

Balbino Barbosa Filho,

Farenzena,

Trierweiler

2024

Preprint

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The main challenges in oil platforms are how to define optimal setpoints and decision-making for controlling variables due to many existing dynamical production uncertainties while conceding safe operation and minimizing discharge to sea and atmosphere without killing plantwide inventory. A novel scheduling model to optimize the whole essential units of an oil platform production gathering topside and subsea variables is disclosed. A proper topside and subsea scheduling model enables and creates harmony between optimal production, supply chain, costs, and market dynamics, satisfying demands, environmental and operations constraints leading to stability. The interest of the present research lies in determining the optimal topside and subsea operational settings and decision-making, establishing maximum E&P performance and production control setpoints while predicting and manipulating reservoir lifespan and its revitalization. This objective is attained through a novel multiperiod large-scale model for the planning and operational production scheduling and model predictive control (MPC) in oil platforms complying with sustainability, profitability, platform design capacities, and offloading of oil and gas to supply chains or to pipeline exportation. The present model details heat and mass transfer, PVT, flow profiles along a planning horizon and can be used to any oil platform since comprises all the essential unit operations for oil, gas, CO2, H2S and water. The model maximizes the total oil production over any planning horizon. The model is versatile, and decision-making can be either linear or nonlinear but rather a MILP or LP as best choice on optimizing large-scale systems. If the model is applied with auto rescheduling on site within variable hours or minutes, it becomes a real-time optimization schedule approach. Due to its high-velocity performance and robustness, a novel multi-objective function strategy, acting as an LP generic plantwide MPC with industrial scope to maximize production while controlling the process is presented, and as an example is here used to control slug flow to avoid equipment trips and inventory instability, at the same time production is maximized. The output from the decision-making was compared to actual plant data, and the results proved compliance to the design capacity with process safety and sustainability. Comparing to official Brazil's government data for oil, the case study showed superiority to the same size platforms, like FPSOs P-75 and MV32, e.g., more than 101.5% more capacity to produce oil in beginning of campaign after first oil, and 10.5% during campaign. The present work indicates that: 1) the production of millions of barrels is being delayed or left aside; 2) not scheduling production harms the environment and diminish process safety; 3) not scheduling production can create stochastic supply chain deliveries instead of organizing it by deterministic offloading days; 4) scheduling is essential to manipulate, revitalize and monitor reservoir's pressure and content under uncertainty; 5) the oil platform can be automatic optimized by a plantwide MPC reducing human activity/dependency; 6) scheduling gives more transparency to stakeholders and contractors by forecasting business data and capability, and can design, develop or enable new businesses.

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“…Lijie Su introduced an innovative continuous-discrete-time hybrid model that stratifies refinery planning and scheduling into hierarchical levels, focusing on multiperiod crude oil scheduling with the aim of maximizing net profits, achieving solution times that range from minutes to hours [19]. Algorithmically, solutions span from MILP-NLP decomposition to solver-integrated responses [20][21][22] and rolling horizon strategies for time-segmented problem-solving [23]. Additionally, intelligent search mechanisms like genetic algorithms have been adopted to bolster solution throughput [24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

State-Space Compression for Efficient Policy Learning in Crude Oil Scheduling

Ma,

Li,

Liu

2024

Mathematics

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The imperative for swift and intelligent decision making in production scheduling has intensified in recent years. Deep reinforcement learning, akin to human cognitive processes, has heralded advancements in complex decision making and has found applicability in the production scheduling domain. Yet, its deployment in industrial settings is marred by large state spaces, protracted training times, and challenging convergence, necessitating a more efficacious approach. Addressing these concerns, this paper introduces an innovative, accelerated deep reinforcement learning framework—VSCS (Variational Autoencoder for State Compression in Soft Actor–Critic). The framework adeptly employs a variational autoencoder (VAE) to condense the expansive high-dimensional state space into a tractable low-dimensional feature space, subsequently leveraging these features to refine policy learning and augment the policy network’s performance and training efficacy. Furthermore, a novel methodology to ascertain the optimal dimensionality of these low-dimensional features is presented, integrating feature reconstruction similarity with visual analysis to facilitate informed dimensionality selection. This approach, rigorously validated within the realm of crude oil scheduling, demonstrates significant improvements over traditional methods. Notably, the convergence rate of the proposed VSCS method shows a remarkable increase of 77.5%, coupled with an 89.3% enhancement in the reward and punishment values. Furthermore, this method substantiates the robustness and appropriateness of the chosen feature dimensions.

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An MINLP formulation for integrating the operational management of crude oil supply

Cited by 17 publications

References 32 publications

Energy Efficiency in Petroleum Supply Chain Optimization: Push Segment Coordination

Energy Efficiency in Petroleum Supply Chain Optimization: Push Segment Coordination

Novel Topside and Subsea Production Optimization, Operational Scheduling, and a Generic Plantwide MPC for Process Systems Control and Production Maximization: Oil, Gas, Water and Utilities Decision-Making with Offloading to Supply Chains and Sustainability

State-Space Compression for Efficient Policy Learning in Crude Oil Scheduling

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