Combining Fix and Relax Heuristic and LP-Metric Method to Solve the Multi-Objective Integrated Production-Routing Problem

Production routing problem is one of the problems of the integrated planning that interests in optimizing simultaneously production, inventory, and distribution planning. This chapter has the purpose of developing two mono-objective models for the production-routing problem: one of them minimizes the total costs which is the classical objective while the other one minimizes the energy consumed by the production system. A bi-objective model is then proposed to combine the two objectives mentioned previously using LP-metric method. To solve big instances of the problem in reasonable time, an approximate approach is proposed using the rolling horizon-based fix and relax heuristic. Finally, computational results are presented to compare the solutions obtained by both approaches.

IMPROVEMENT OF ROBOTIC SYSTEMS BASED ON VISUAL CONTROL

The subject of research in the article is the use of adaptive visual control in flexible integrated robotic systems. The goal of the work is the integration of visual control facilities into automated control systems for transport and handling operations of flexible integrated production. The article solves the following tasks: analyze the application of visual control methods in robotics, consider methods for improving adaptive visual work control systems, formulate the basic requirements for adaptive visual control systems, and develop a control model for a mobile robot in the space of a flexible integrated production systems and computer vision systems. To solve the set tasks, the methods of set theory, methods of automatic control theory, and methods of the theory of image processing were used. The following results were obtained: the analysis of visual control systems was carried out from the point of view of solving the problems of flexible integrated systems of modern production; the adaptive visual control scheme was improved by introducing a declarative workspace model and a functional model of a flexible integrated system; the main requirements and tasks of adaptive visual control systems are formulated; considered the main stages of processing visual information and their practical implementation, including multi-zone workspaces; a model of visual control of a mobile robot in a flexible integrated production workspace has been developed; the practical tasks of managing mobile platforms have been solved. Conclusions: the use of adaptive visual control in a production environment will allow combining the elements of flexible integrated production distributed in space, providing monitoring, control and refinement of control processes in real time, the functioning of intelligent control tools, which will improve the quality of control processes.

Unifying of Steady State and Transient Simulations Methodologies for Increasing Oil Production of Integrated Network of Wells, Pipeline and Topside Processing Equipment

Aging facilities and increasing complexity in operations (e.g., increasing water cut, slugging, sand or wax production) continue to widen the gap between actual production and the full potential of the field. To enable production optimization scenarios within an integrated system comprises of reservoirs, wells and surface facilities, the application of an integrated network modelling has been applied. The highlight of this paper is the synergy of Integrated Production Network Modelling (IPNM) utilizing Steady State Simulator (PROSPER-GAP) and the Transient Simulator (OLGA) tools to identify potential quick gains through gaslift optimization as well as mid and long-term system optimization alternatives. The synergy enables significant reduction in transient simulation time and reduced challenges in OLGA well matching, especially in selecting accurate modelling parameters e.g., well inflow performance (validated well (string) production data, reservoir pressure, temperature and fluid properties and the Absolute Open Flow (AOF) of each well). The paper showcased the successful production gain achieved as well as the workflows and methodologies applied for both Steady State Integrated Production Modelling (IPM Steady State) and Integrated Transient Network Modelling (IPM Transient) as tools for production enhancement. Even though IPM Steady State shows promising results in term of field optimization potential, to increase accuracy and reduce uncertainties, IPM Transient is recommended to be performed to mimic the actual transient phenomena happening in the well to facilities

Application of Machine Learning Algorithms and Integrated Production Modelling to Improve Accuracy of Liquid Production Rate Measurements Using Multiphase Flow Meters

Objectives/Scope: During the period of two years the difference between sum of daily oil flow rate measurements of each oil production well using multiphase flow meter (MPFM) and cumulative daily oil production rate measured by custody transfer meter increased overall by 5%. For some wells inaccuracy of MPFM liquid rate measurement could reach 30-50%. The main goal of this research was to improve the accuracy of multiphase flow meter production rate measurements. Methods, Procedures, Process: More than 80 oil production wells were involved in the research, more than 100 flow rate tests were carried out. Machine learning methods such as supervised learning algorithms (linear and nonlinear regressions, method of gradient descent, finite differences algorithm, etc.) have been applied coupled with Integrated production modelling tools such as PROSPER and OpenServer in order to develop a function representing correlation between MPFM parameters and flow rate error. Results, Observations, Conclusions: The difference between cumulative daily oil production rate measured by custody transfer meter and multiphase flow meters decreased to 0.5%. The solution has been officially applied at the oil field and saved USD 500K to the Company. The reliability of the function was then proved by the vendor of MPFMs. Novel/Additive Information: For the first time machine learning algorithms coupled with Integrated Production modelling tools have been used to improve the accuracy of multiphase flow meter production rate measurements.