A framework for the near-real-time optimization of integrated oil & gas midstream processing networks

The oil and gas industry plays a key role in the world’s economy. Vast quantities of crude oil, their by-products and derivatives are produced, processed and distributed every day. Indeed, producing and processing significant volumes of crude oil requires connecting to wells in different fields that are usually spread across large geographical areas. This crude oil is then processed by Gas Oil Separation Plants (GOSPs). These facilities are often grouped into clusters that are within approximate distance from each other and then connected laterally via swing lines which allow shifting part or all of the production from one GOSP to another. Transfer lines also exist to allow processing intermediate products in neighbouring GOSPs, thereby increasing complexity and possible interactions. In return, this provides an opportunity to leverage mathematical optimization to improve network planning and load allocation. Similarly, in major oil producing countries, vast gas processing networks exist to process associated and non-associated gases. These gas plants are often located near major feed sources. Similar to GOSPs, they are also often connected through swing lines, which allow shifting feedstock from some plants to others. GOSPs and gas plants are often grouped as oil and gas midstream plants. These plants are operated on varied time horizons and plant boundaries. While plant operators are concerned with the day-to-day operation of their facility, network operators must ensure that the entire network is operated optimally and that product supply is balanced with demand. They are therefore in charge of allocating load to individual plants, while knowing each plants constraints and processing capabilities. Network planners are also in charge of producing production plans at varied time-scales, which vary from yearly to monthly and near-real time. This work aims to establish a novel framework for optimizing Oil and Gas Midstream plants for near-real time network operation. This topic has not been specifically addressed in the existing literature. It examines problems which involve operating networks of GOSPs and gas plants towards an optimal solution. It examines various modelling approaches which are suited for this specific application. It then focuses at this stage of the research on the GOSP optimization problem where it addresses optimizing the operation of a complex network of GOSPs. The goal is to operate this network such that oil production targets are met at minimum energy consumption, and therefore minimizing OpEx and Greenhouse Gas Emissions. Similarly, it is often required to operate the network such that production is maximized. This thesis proposes a novel methodology to formulate and solve this problem. It describes the level of fidelity used to represent physical process units. A Mixed Integer Non-Linear Programming (MINLP) problem is then formulated and solved to optimize load allocation, swing line flowrates and equipment utilization. The model demonstrates advanced capabilities to systematically prescribe optimal operating points. This was then applied to an existing integrated network of GOSPs and tested at varying crude oil demand levels. The results demonstrate the ability to minimize energy consumption by up to 51% in the 50% throughput case while meeting oil production targets without added capital investment.Open Acces

Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009

Dieser Tagungsband enthält die Beiträge des 19. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) und der Fachgruppe „Fuzzy-Systeme und Soft-Computing“ der Gesellschaft für Informatik (GI), der vom 2.-4. Dezember 2009 im Haus Bommerholz bei Dortmund stattfindet

Model-based Calibration of Engine Control Units Using Gaussian Process Regression

Reducing the number of tests on vehicles is one of the most important requirements for increasing cost efficiency in the calibration process of engine control units (ECU). Here, employing virtual vehicles for a model-based calibration of ECUs is essential. Modelling components for virtual vehicles can be a tedious and time-consuming task. In this context, data-based modelling techniques can be an attractive alternative to physical models to increase efficiency in the modelling process. Data-based models can incorporate unknown nonlinearities encoded in the sampled data, resulting in more accurate models in practice. In combination with automated measurement, data-based modelling can help to significantly accelerate the calibration process. Furthermore, the fast simulation speed of the resulting models allows their implementation into real-time simulation environments, such as Hardware-in-the-Loop (HiL) systems, and thus enables a model-based calibration of the related ECU software function. However, generating appropriate data for learning dynamic models, i.e., the transient Design of Experiments (DoE), is not straightforward, since system boundaries and permissible excitation frequencies are not known beforehand. Thus the training data of the system measurement will be inconsistent and the main challenge of the identification process is to deal with this data to achieve a globally valid model. Furthermore, when dealing with dynamic systems in an automotive context, the Engine Control Unit typically changes operating modes while driving. Thus nonlinearities and changes of physical structures appear, which need to be considered in the model. In this thesis, a modelling system called the Local Gaussian Process Regression (LGPR), is used and adapted in order to receive a flexible modelling approach, which allows an iterative modelling process and obtains robust and globally valid dynamic models. The adapted LGPR approach is employed for the ECU calibration of dynamical automotive systems, which is critical regarding system excitation. Using LGPR, it is possible to measure the system iteratively while exploring the relevant state-space regions and improving the quality of the model step by step. The results show that LGPR is beneficial for iterative modelling of dynamical systems. Compared to the traditional Gaussian Process Regression (GPR) modelling approach, LGPR yields better results regarding the variable system dynamics