Recent developments in mathematical techniques have allowed the development of a software package to simulate and optimise the flow of hydrocarbons through oil and gas production and distribution networks of arbitrary connectivity and complexity. It has been successfully applied to a wide variety of operational problems including de-bottlenecking, optimisation of compressor strategies and determining the optimal lift-gas allocation to networks of gas lifted wells. This paper discusses the innovative mathematical concepts at the heart of the algorithm taking account of both optimisation and modelling aspects. It demonstrates how an integrated approach to optimisation and simulation based on Sequential Linear Programming (SLP) techniques provides the user with substantially enhanced flexibility and reliability compared with traditional nodal analysis type approaches and how these benefits can be realised in concrete practical examples. Descriptions of the behaviour of physical objects in a production or distribution network is commonly given in the form of measured data sets or discrete performance tables rather than analytical functions. A final focus of this paper is the discussion of how such data sets can be used directly and efficiently in an optimisation context. Introduction Throughout field life hydrocarbon production facilities need to be designed, built, maintained and altered to meet the developing needs of the hydrocarbon reservoirs to which they are connected. Computer models of the behaviour of the facilities are an essential pre-requisite to the efficient management of this process. In this paper we introduce a simulation and optimisation method for hydrocarbon production networks based on a technique called sequential linear programming (SLP). In the sections below we first introduce the concept of SLP and how it can be applied to hydrocarbon production. We then proceed to discuss the key advantages of our technique over other more traditional methods. Additionally, we point out how recent developments in mathematical modelling and optimisation have allowed us to enhance the robustness and speed of our approach. Since its commercial release in early 1999 our technique has been successfully applied to a wide variety of production system design and optimisation problems. For this paper we have chosen two recent case studies. The first is a de-bottlenecking study to enhance the production rates achieved from a medium sized gas gathering network. The second study demonstrates how the software package can be used to optimise the usage of produced associated gas in a highly complex multiphase hydrocarbon production system. Sequential Linear Programming (SLP) Sequential linear programming is a family of techniques for the solution of large-scale non-linear optimisation and simulation problems. The philosophy of the approach was first introduced by Griffith and Stewart who applied it to hydrocarbon processing systems.1 For a recent description of the approach see e.g. Bazaraa et al.2

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractRecent developments in mathematical techniques have allowed the development of a software package to simulate and optimise the flow of hydrocarbons through oil and gas production and distribution networks of arbitrary connectivity and complexity. It has been successfully applied to a wide variety of operational problems including de-bottlenecking, optimisation of compressor strategies and determining the optimal lift-gas allocation to networks of gas lifted wells. This paper discusses the innovative mathematical concepts at the heart of the algorithm taking account of both optimisation and modelling aspects. It demonstrates how an integrated approach to optimisation and simulation based on Sequential Linear Programming (SLP) techniques provides the user with substantially enhanced flexibility and reliability compared with traditional nodal analysis type approaches and how these benefits can be realised in concrete practical examples. Descriptions of the behaviour of physical objects in a production or distribution network is commonly given in the form of measured data sets or discrete performance tables rather than analytical functions. A final focus of this paper is the discussion of how such data sets can be used directly and efficiently in an optimisation context.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractAn integrated solution was required to optimise the use of gas in a complex network installed for gas lifting operations, gas export and reservoir re-injection. The solution was required because the compression facilities represent a major element of the cost of operating the field and therefore it is important that the high pressure gas is used as efficiently as possible. It is also important that recommendations from the optimiser are implemented with minimal delay and therefore closed-loop optimisation was selected, which requires that the optimiser is integrated with the field's SCADA system.The complexity of the system to be optimised presented a unique challenge. Traditional nodal analysis modelling techniques are generally not robust when applied to complex, non-hierarchical networks. Consequently, the Sequential Linear Programming (SLP) optimisation technique is applied to solve the computer model of the low pressure gas gathering system, multi-stage compression facilities and high pressure gas distribution system. The novel implementation of this technique simultaneously simulates and optimises since it is able to select the best combination of pressures and flowrates from the set of all combinations which will satisfy the problem constraints.This paper describes the development of the optimisation application, the network and well models and the underlying mathematical techniques. The integration with SCADA and other databases is described, along with a discussion of how the form of the integration reflects the desired workflow for operating and optimising the field. Finally, the paper provides a quantitative assessment of the benefits realised by applying the optimisation application in the field. These benefits are obtained through increased oil production, reduced compressor fuel gas consumption and reduced venting of low pressure gas.

An in-situ combustion (ISC) pilot project was operated in the Quifa heavy oil reservoir in Colombia from Nov 2011 to July 2014. A modified inverted-nine spot pattern was used. As part of the project all wells were instrumented with pressure and temperature devices.Parameters such as bottom hole temperature and pressure, gas composition (N2, CO2, CO, O2, SO4, H2S and hydrocarbons), water composition (minerals, pH), oil gravity and gas, oil and water production rates were measured and analyzed daily in order to control the combustion process resulting in improving the volumetric sweep efficiency and oil recovery factor. The continuing monitoring of these variables helped redirecting the combustion front, optimizing air injection and increasing production. This paper introduces the STAR™ (Synchronized Thermal Additional Recovery) technology, based on ISC concepts, which aim increase the recovery factor and creating value in ISC in a heavy oil reservoir. STAR™ is based on the Synchronization Integrated Model (SIM), a suite of software applications which help to generate the main combustion-related parameters such as H/C ratio, oxygen utilization, air-oil ratio (AOR), air requirement, etc.; evaluate the process performance and identify the position of the combustion and fluids fronts in real time.

This paper was prepared for presentation at the 1999 SPE Western Regional Meeting held in Anchorage, Alaska, 26–28 May 1999.