Offshore oil production planning optimization: An MINLP model considering well operation and flow assurance

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Abstract

With the increasing energy requirement and decreasing onshore reserves, offshore oil production has attracted increasing attention. A major challenge in offshore oil production is to minimize both the operational costs and risks; one of the major risks is anomalies in the flows. However, optimization methods to simultaneously consider well operation and flow assurance in operation planning have not been explored. In this paper, an integrated planning problem both considering well operation and flow assurance is reported. In particular, a multi-period mixed integer nonlinear programming (MINLP) model was proposed to minimize the total operation cost, taking into account of well production state, polymer flooding, energy consumption, platform inventory and flow assurance. By solving this integrated model, each well's working state, flow rates and chemicals injection rates can be optimally determined. The proposed model was applied to a case originated from a real-world offshore oil site and the results illustrate the effectiveness.

Introduction

Crude oil is the major energy resource in the modern society and continues to be so in the coming years (Kang et al., 2017). It is typically produced by drilling production wells in large oil fields with several reservoirs. Onshore hydrocarbon resources have become increasingly scarce with the continuous exploitation of the past decades. At the same time, the sea contains vast oil and gas resources. The exploitation and usage of offshore oil resources are receiving more and more attention. In general, deep-water oil reserves are difficult to exploit accompanied with large production costs due to the harsh environment and the energy intensity required for the production (Narimanov, 2008; Zhu et al., 2018; Wang et al., 2017). Therefore, there are clear incentives to seek more efficient operations while reducing the risks. To this end, optimization approaches for production planning and scheduling have received increasing attention from both the academic and industrial communities (Hou, 2014; Gao et al., 2018a, 2018b; Wang et al., 2016).

In the literature, significant progress has been reported for the scheduling and planning of oil production processes. Gupta and Grossmann (2012) built an efficient strategic/tactical planning multi-period MINLP model for offshore production optimization with the objective of maximizing the total net present value (NPV), considering three components (oil, gas and water), FPSO (floating production, storage and offloading) topside's inventory level and the well's production rate. Ortı́z-Gómez et al., 2002 investigated the oil production planning problem in the wells of an oil reservoir considering nonlinear behavior of the well flowing pressure with respect to time. Heever et al. (2000) considered nonlinear reservoir behavior and its impact on the complex business aspects, and proposed a MINLP model for offshore oil facility design and planning. An integrated MILP model for making a group of strategic decisions about oil and gas development projects simultaneously over a long-term planning horizon was proposed by Shakhsi-Niaei et al. (2014), where production planning, upstream transmission planning and their interactions with projects selection and sequence are addressed. Kosmidis et al., 2005 presented a mixed integer nonlinear (MINLP) model for daily well scheduling in oil fields, where the nonlinear reservoir behavior, the multiphase flow in wells and constraints from the surface facilities are considered to decide the operational status of wells (i.e. open or closed), the allocation of wells to manifolds or separators, the allocation of flow lines to separators, the well oil rates and the allocation of gas-to-gas lift wells. Carvalho and Pinto (2006) proposed an MILP approach, reformulated from an MINLP model, to determine the assignment of platforms to wells and the timing for fixed assignments. In another study, a novel approach to scheduling the startup of oil and gas wells in multiple fields over a decade-plus discrete-time horizon was presented (Kelly et al., 2017). The major innovation was to treat each well or well type as a batch-process with time-varying yields or production rates that follow the declining, decaying or diminishing curve profile. Tavallali and Karimi (2016) developed an MINLP approach for more holistic decisions on the order, placement (Ozdogan and Horne, 2006; Tavallali, 2013), timing, capacities, and allocations of new well drillings and surface facilities such as manifolds, surface centers, and their interconnections, along with well production/injection profiles. Ortı́z-Gómez et al., 2002 described three mixed integer multi-period optimization models of varying complexity for the oil production planning in the wells of an oil reservoir in order to determine the oil production profiles and operation/shutdown of the wells in each time period. Moreover, an oil well production scheduling problem for the light load oil well during exploitation was studied, which was to determine the turn on/off status and oil flow rates of the wells in a given oil reservoir, subject to a number of constraints such as minimum up/down time limits and well grouping (Lang and Zhao, 2016). Iyer et al. (1998) presented a MILP model for the planning and scheduling of investment and operation in offshore oil field, in which the net present value is taken as objective function and the choice of reservoirs to develop, the well drilling and platform installation schedule, capacities of each well and production platform, and the fluid production rates from wells are taken as decision variables.

In the field of oil production process optimization, the existing results mainly focused on onshore but very little has been done on the offshore oil production processes, especially for deep water. The above-reviewed studies, whilst often shedding insight into the various aspects of the challenge, are not suitable for direct application in practice. A major limitation is that most of them considered only one or a few sections of the entire production system, such as the well type and location, production rates, status of oil wells, the allocation of flow lines (Yeten et al., 2002; Gunnerud and Foss, 2010; Aseeri et al., 2004; Ulstein et al., 2007), polymer flooding process, artificial lift process (Hallundbæk, 2016) and flow assurance (Luna-Ortiz et al., 2008; Zhou et al., 2014). Flow assurance refers to ensuring successful and economical flow of hydrocarbon stream from reservoir to the point of sale or storage, which is widely viewed as a major challenge for offshore oil and gas production (e.g. due to hydrate formation and wax deposition in the pipe). To the best of our knowledge, integrated planning optimization that consider both facility operation and flow assurance has not been reported in the literature, despite that the topic is of great importance to ensure safety, in particular for offshore oil and gas production.

The particular challenge to be addressed in this work is the flow assurance, in contrast to the existing focus on subsea exploitation equipment operation aiming for maximum yield. It is well known that a change of well operations results in varying flowrate in subsea pipelines, thus has a big impact on the subsequent multiphase flow transportation processes. Therefore, in this work, a multi-period mathematical model involving well operation and flow assurance for the planning optimization of offshore oil production is presented. We propose a discrete time representation based entire process planning model including the subsea production process, polymer flooding process (Wang et al., 2005), flow assurance (Hou and Zhang, 2004), platform storage of oil and delivery process. The rest of this paper is organized as follows. First, the problem statement and process description are given in Section 2. On the basis of process analysis, Section 3 provides the detailed entire process planning model. A case study from a real-world production process is presented to demonstrate the feasibility of the proposed MINLP model in Section 4. Finally, conclusions are drawn in Section 5.

Section snippets

Process description

From the wells to the platform, the whole production process can generally be divided into three parts: the under-well reservoir process, the under-water production process and the over-water platform section (Fig. 1).

Oil field is composed by a large number of wells which can spread over a wide geographical area. Usually, one oil field contain a lot of reservoirs, each of which contains many wells. The wells can be divided into different batches of oil wells by close geographic location which

Mathematical model

The integrated planning model defined as a multi-period MINLP has been developed considering both well operation and flow assurance, taking the minimum value of the total operating costs over the planning horizon as the objective function while satisfying all the constraints.

Several assumptions are made in this study as follows:

  • (1)

    The production wells are separated and totally independent of each other. It is natural because each well has its own independent reservoir.

  • (2)

    During the middle and later

Description of the case

The model is tested on a case originated from a real-world subsea oil site in China to verify the effectiveness of proposed model. The site has 12 oil wells split into 3 well batches depending on their geographic location, where the wells 1#~4#, 5#~8# and 9#~12# are grouped into three different batches respectively. Table 1 shows the monthly demands of 3 oil well batches. The planning horizons are 12 months. The parameters used in the case, such as production rate limits of each oil well, max

Conclusion

In this paper, the study has addressed the integrated optimization of both plant-wide production process. An MINLP planning optimization model is proposed for a real-world practical deep sea oil production in a discrete time period which is aimed to minimize the cost of whole oil production process. The proposed model can reflect start-stop operation of oil wells to reduce unnecessary costs. Energy consumption has been taken into consideration by modeling the diesel consumption of diesel

CRediT authorship contribution statement

Xiaoyong Gao: Conceptualization, Methodology, Writing - original draft. Yi Xie: Data curation, Formal analysis, Software, Writing - original draft. Shuqi Wang: Data curation, Methodology, Formal analysis. Mingyang Wu: Formal analysis. Yuhong Wang: Conceptualization. Chaodong Tan: Methodology, Formal analysis. Xin Zuo: Investigation, Visualization. Tao Chen: Conceptualization, Supervision, Writing - review & editing.

Declaration of Competing Interest

The authors declare no competing financial interest.

Acknowledgments

This research was supported by National Key R&D Program of China (No. 2016YFC0303703), the National Natural Science Foundation of China (No. 21706282), Science Foundation of China University of Petroleum, Beijing (No. 2462017YJRC028) and the UK EPSRC (EP/R001588/1).

References (35)

  • M.C.A. Carvalho et al.

    A bilevel decomposition technique for the optimal planning of offshore platforms

    Braz. J. Chem. Eng.

    (2006)
  • X.Y. Gao et al.

    Plant planning optimization under time-varying uncertainty: case study on a polyvinyl chloride plant

    Ind. Eng. Chem. Res.

    (2018)
  • X.Y. Gao et al.

    Piecewise linear approximation based MILP method for PVC plant planning optimization

    Ind. Eng. Chem. Res.

    (2018)
  • V. Gupta et al.

    An efficient multiperiod MINLP model for optimal planning of offshore oil and gas field infrastructure

    Ind. Eng. Chem. Res.

    (2012)
  • Hallundbæk J. (2016). Artificial lift tool. United States Patent...
  • S.A.den Heever et al.

    Integrating complex economic objectives with the design and planning of offshore oilfield infrastructures

    Comput. Chem. Eng.

    (2000)
  • R.N. Horne

    Modern Well Test Analysis: A Computer-Aided Approach

    (1990)
  • View full text