#02 Crude Oil Supply Chain Simulation: How Can Feedstock Deliveries to Refineries Be Simulated?

       Efficient operation of refineries requires stable feedstock supplies from oil and gas fields. Depending on the characteristics of the production system, the hydrocarbon feedstock may include crude oil, natural gas, and unstable gas condensate transported primarily through a pipeline network.
       Planning feedstock deliveries to refineries is a challenging task because hydrocarbon production plans must be aligned with downstream processing capabilities and the capacity constraints of process units and tank farms at the processing facility. Differences in the composition and quality of feedstock supplied from different oil and gas fields further complicate refinery planning and production planning.
       Under these conditions, crude oil supply chain simulation plays an important role in analyzing delivery operations and the behavior of the entire supply network. By representing oil and gas fields, transportation infrastructure, and refineries as interconnected objects within a simulation model, engineers can identify bottlenecks, evaluate the consequences of operational decisions, perform what-if analysis, and address supply chain optimization problems.
       This article discusses approaches to the crude oil supply chain simulation of feedstock deliveries to refineries involving crude oil, natural gas, and unstable gas condensate. It also describes the capabilities required to develop realistic refinery simulation models and support digital twin development for hydrocarbon feedstock supply systems.

       Modern refineries typically receive hydrocarbon feedstock from multiple oil and gas fields. Crude oil, natural gas, and unstable gas condensate are transported through a pipeline network and may pass through storage tanks and tank farms before entering processing facilities.In many cases, however, buffer capacity is limited or completely absent, making accurate planning and assessment of incoming feedstock volumes critical to the operation of the entire production chain.
       Differences in feedstock properties and processing capabilities further complicate refinery planning. Some units are designed only for light feedstocks, while others require heavy feedstocks or specific blending conditions. As a result, prioritizing feedstock flows from oil and gas fields becomes essential. Certain streams may have higher priority because of natural gas production requirements or their higher content of valuable components.
       Another important aspect is flow smoothing. Sudden changes in production rates may cause disturbances in pipelines, storage tanks, and refinery units. Excessively rapid flow variations can reduce system stability and result in unrealistic behavior in a simulation model.
       In addition to normal operating conditions, engineers must consider reduced production, field shutdowns, changes in demand for refined products, and other disturbances.

       Crude oil, natural gas, and unstable gas condensate are transported through a pipeline network to processing facilities, where they may be accumulated in tank farms before being fed to process units. In a simplified form, the structure of such a feedstock supply chain can be represented as follows:

       Each field has its own production profile, while the pipeline network imposes transportation and blending constraints. Storage tanks provide buffering and flow smoothing, whereas the refinery defines requirements for feedstock quantity and quality. In practice, crude oil supply chain systems are much more complex. They may include multiple oil and gas fields, several pipelines, blending points, intermediate tank farms, and alternative transportation routes. In addition, feedstock prioritization rules, changing production rates, and equipment limitations must be taken into account. Therefore, individual facilities cannot be analyzed in isolation. Dynamic simulation provides an effective way to investigate interactions across the entire feedstock supply chain and support refinery planning and supply chain optimization.

       Simulation represents a feedstock supply system as a set of interconnected objects that reproduce the behavior of real production processes. In addition to flow rates, feedstock streams may be characterized by their composition, type, and physicochemical properties, which are automatically recalculated as the flow passes through different parts of the simulation model. Unlike static calculations, dynamic simulation enables engineers to account for variations in production rates, accumulation and withdrawal from storage tanks, and to perform scenario analysis under different operating conditions. For example, the impact of reduced production, changing demand, or infrastructure constraints can be evaluated.
       Another important advantage is that all elements of the model are interconnected. Constraints arising in one part of the system automatically affect other facilities, eliminating the need to create separate independent models and subsequently reconcile their results.
       Thus, simulation provides a powerful tool for analyzing the dynamic behavior of the entire feedstock supply chain. It supports decision support systems, what-if analysis, and digital twin development for crude oil supply chain simulation, refinery planning, and supply chain optimization.

       One of the characteristic features of hydrocarbon feedstock supply systems is the variability of crude oil, natural gas, and unstable gas condensate production. Unlike simplified calculations that assume constant flow rates, real production systems operate under continuously changing production conditions.
       Production rates are influenced by well operating modes, maintenance activities, seasonal factors, and constraints in adjacent facilities. As a result, actual feedstock deliveries may vary over time, affecting pipeline utilization, storage tanks and tank farms, as well as refinery throughput.
       Dynamic simulation makes it possible to account for variable production rates by defining feed flows through time-dependent functions, production schedules, or external calculations. This approach supports scenario analysis under different operating conditions and improves the accuracy of digital twin development and decision support systems used in refinery planning and production planning.

       In real feedstock supply systems, different oil and gas fields and supply sources often have different priorities. These priorities may be driven by economic considerations, process constraints, feedstock quality, or production planning requirements. For example, a refinery may seek to maximize feedstock deliveries from one field while compensating for shortages with supplies from other sources. In the event of reduced production, the system must automatically redistribute flows among available suppliers to maintain the desired refinery throughput.
       Additional complexity arises when multiple feedstock streams compete for limited transportation or processing capacities. Under such conditions, the order in which available resources are utilized has a significant impact on the performance of the overall feedstock supply chain.
       Simulation models make it possible to represent different feedstock prioritization strategies and evaluate their effects. Depending on the application, fixed priorities, proportional allocation, or more sophisticated control algorithms may be used. Analysis of alternative prioritization strategies helps assess system resilience, evaluate the impact of constraints on refinery operation, and identify more efficient approaches to refinery planning and supply chain optimization.

       Production rates at oil and gas fields often fluctuate, while refineries require a more stable supply of crude oil, natural gas, and unstable gas condensate. Large variations in flow rates may reduce operating efficiency and create additional process constraints.
The need for flow smoothing is driven not only by real production conditions but also by the structure of input data. In practice, production plans are often available as daily or monthly averages. When such aggregated data are used in detailed simulations, unrealistic flow jumps may appear at the boundaries between periods. Direct use of aggregated data can therefore produce artificial fluctuations in storage levels, pipeline loading, and refinery operation. To address this issue, simulation models incorporate flow smoothing mechanisms that ensure gradual changes in flow rates and improve the realism and stability of calculations. By analyzing different flow smoothing strategies, engineers can evaluate the required storage capacity and better reproduce the behavior of real feedstock supply systems.

       One of the major advantages of simulation is the ability to investigate different operating scenarios for a feedstock supply chain. A simulation model can be used to analyze the effects of reduced production at individual oil and gas fields, pipeline capacity constraints, changes in feedstock prioritization, or increased feedstock demand from a refinery. The reverse problem can also be addressed by imposing constraints on feedstock intake, for example due to equipment failures, maintenance activities, or reduced processing capacity. In this case, the model can evaluate how such limitations affect the entire crude oil supply chain, including pipeline utilization, storage tank levels, and production rates at upstream facilities. Thus, simulation provides powerful what-if analysis capabilities, enabling engineers to identify bottlenecks and evaluate alternative operating strategies. The results can be used for production planning, refinery planning, supply chain optimization, and digital twin development. As a result, crude oil supply chain simulation becomes a powerful decision support tool for modern refineries.

       One of the most important applications of crude oil supply chain simulation is refinery planning and production planning. Simulation models make it possible to evaluate feedstock deliveries to refineries, analyze equipment and pipeline constraints, and assess the impact of changing production rates and feedstock composition on refinery performance. Such capabilities help engineers identify bottlenecks and support more efficient operation of the entire crude oil supply chain.
       In addition, simulation models provide powerful what-if analysis capabilities for comparing alternative operating strategies and assessing the consequences of equipment outages, reduced feedstock availability, and changing demand. As a result, crude oil supply chain simulation has become an essential tool for refinery planning, production planning, supply chain optimization, and digital twin development in modern refining systems.

       Crude oil supply chain simulation plays an important role in digital twin development for modern feedstock supply systems. By combining operational data with a simulation model, engineers can create a digital twin that reproduces the behavior of oil and gas fields, pipeline infrastructure, storage facilities, and refineries. Such models support refinery planning, production planning, and what-if analysis while providing a realistic representation of feedstock deliveries to refineries.
       Digital twin solutions also enable continuous monitoring, what-if analysis, and supply chain optimization under changing operating conditions. By analyzing alternative scenarios and evaluating the effects of equipment limitations, changing production rates, and feedstock prioritization strategies, engineers can improve the reliability and efficiency of the entire crude oil supply chain. As a result, refinery simulation and digital twin development have become key technologies for modern decision support systems in the oil and gas industry.

       The principles discussed above can be implemented in a simulation model describing the interaction between oil and gas fields, pipeline infrastructure, tank farms, and a refinery. Such models can account for variable production rates, feedstock prioritization, flow smoothing, and different operating scenarios. The Petroleum Refining Library (PRL) provides specialized Source components for modeling feedstock deliveries of crude oil, natural gas, and unstable gas condensate. These components support varying flow rates, stream composition, priorities, and other features of real production systems.
       An example of such a refinery simulation model based on Petroleum Refining Library is presented in the Solutions section. It demonstrates an approach to crude oil supply chain simulation involving feedstock deliveries to refineries from multiple oil and gas fields and illustrates how Petroleum Refining Library can be used for refinery planning, scenario analysis, production planning, and digital twin development.

       Variable production rates, transportation constraints, and feedstock prioritization significantly complicate the management of modern feedstock supply chains. Crude oil supply chain simulation makes it possible to analyze feedstock deliveries to refineries, investigate alternative operating scenarios, and support production planning and refinery planning. As a result, simulation models have become an important tool for supply chain optimization and digital twin development in modern refining systems.