海外油氣效益產量決策模型研究及應用

李婷

doi:10.13374/j.issn2095-9389.2023.03.15.003

海外油氣效益產量決策模型研究及應用

doi: 10.13374/j.issn2095-9389.2023.03.15.003

李婷^,

中國石油化工股份有限公司勘探開發研究院，北京 100083

基金項目: 中國石油化工股份有限公司科技部項目（P19020-3）

詳細信息

通訊作者:
E-mail: liting.syky@sinopec.com

中圖分類號: F270.3
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出版歷程
- 收稿日期: 2023-03-15
- 網絡出版日期: 2023-04-27
- 刊出日期: 2023-10-25

Development and application of an optimization model for overseas oil and gas production benefits

LI Ting^,

Research Institute of Exploration and Development, SINOPEC, Beijing 100083, China

More Information

Corresponding author: E-mail: liting.syky@sinopec.com

摘要

摘要: 效益最大化是國際石油公司生產經營的永恒主題，油氣產量是效益實現的載體，提高效益產量則是海外資產保值增值的必然途徑. 針對目前國內公司對于海外項目開展提質增效的一系列做法，亟待建立一套能夠兼容油價震蕩、適應海外項目，并滿足不同需求的綜合效益產量決策方法，助力海外項目提質增效. 針對海外項目不同于國內項目的特點，分析了礦稅制、產量分成、服務合同等不同油氣項目合同模式下的效益實現特點及策略；并基于國內外調研分析，建立了一套不同效益條件（成本、產量等指標浮動）下的海外項目效益產量評價邏輯框架，以整體邊際效益、現金流、利潤優化目標為決策點，指導效益配產，實現資產增值保值；在兼顧收益性與風險性的基礎上，創建全效益多維度效益產量決策模型并設計求解算法，在滿足石油公司的投資、成本等多種約束條件下，考慮產量、利潤、風險等多個決策目標，給出海外油氣田項目開發的全維度最優決策區間，即帕累托解集. 將創建的模型應用于海外油田具體案例，給出一定決策目標下的帕累托效益最優決策區間，并對解集中的每個解進行深度分析比選，提出按不同決策偏好選取不同的對應解，從而滿足效益經營決策的客觀性及科學性. 最后考慮不確定性因素的影響，分情景對方案產量、油價、成本及投資等的不確定性進行分析，取得較好的應用效果，為制定海外油田效益產量優化方案、資產保值增值提供可靠的決策支持.
- 全效益多維度 /
- 效益產量決策模型 /
- 資產保值增值 /
- 帕累托解集 /
- 最優決策區間
Abstract: Benefit maximization is the enduring objective of production and management for international oil companies. This objective can only be realized through oil and gas production; thus, enhancing production efficiency inevitably leads to maintaining and increasing the value of overseas assets. Given the current practices adopted by domestic companies to improve the quality and efficiency of overseas projects, it is imperative to establish a set of comprehensive benefit and output optimization methods that can withstand oil price shocks, adapt to overseas projects, and cater to various other requirements, thereby serving to improve the quality and efficiency of overseas projects. Taking into account the differences between overseas and domestic projects, this paper analyzes the characteristics and strategies for realizing benefits under different financial and tax regimes, such as mine tax contracts, output-sharing contracts, and service contracts. A framework for evaluating the benefits and outputs of overseas projects under different conditions (such as cost, output, and other floating indicators) is established based on research and analysis conducted at home and overseas. Overall marginal benefit, cash flow, and profit optimization objectives are used to guide benefit allocation to achieve asset appreciation and preservation. A multidimensional and multi-objective decision-making model and an algorithm for benefit maximization are developed by considering both profitability and risk. A Pareto solution set is provided for a comprehensive optimal decision-making interval of overseas oil and gas field project development by considering the constraints of investment, cost, and block in conjunction with multiple decision-making objectives such as production, profit, and risk. This model was applied to specific cases of overseas oil fields under certain decision objectives, and the Pareto optimal decisions were generated. An in-depth analysis and comparison of each solution in the solution set was conducted. Appropriate selection of different solutions is advocated for different decision preferences to ensure the objectivity and scientific nature of profit management decisions. Finally, considering the influence of uncertainty factors, the scenario-based uncertainty analysis of the project output, oil price, cost, and investment has proved effective and generally provides reliable support for decision making in the context of overseas oilfield efficiency and plans for production optimization and high-quality development. The benefit production model and solving algorithm for overseas oilfield projects developed in this paper can provide a theoretical basis and support for decision making by oil field companies to optimize the production benefits of overseas oil fields and improve profitability.
- full benefit multidimensional /
- benefit yield model /
- asset appreciation and preservation /
- Pareto solution set /
- optimal decision interval

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圖 1 遺傳算法流程圖

Figure 1. Flowchart of the genetic algorithm

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圖 2 效益產量優選解對于效益提升的貢獻

Figure 2. Contribution of optimal solution for benefit production to benefit enhancement

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圖 3 總產量概率分布及統計量

Figure 3. Probability distribution and statistics for the total output

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表 1 效益產量模型要素集

Table 1. Element set of the benefit yield model

Element	Interpretation
$ {x}_{ij} $	Whether to exploit the jth field project in the ith block
$ {q}_{ij} $	Maximum production from the jth field project in the ith block
$ {Q}_{\mathrm{t}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{t}} $	Total target production
$ {p}_{ij} $	Net profit of the jth oilfield project in the ith block
$ {c}_{ij} $	Net cash flow from the jth oilfield project in the ith block
$ {r}_{ij} $	Combined risk for the jth field project in the ith block, which is a weighted average of field reserve risk, political risk, and oil price risk, weighted and scored by experts in field development planning
$ {i}_{ij} $	Investment in the jth oil field project in the ith block
$ {o}_{ij} $	Operating cost of the jth oilfield project in the ith block
$ {m}_{ij} $	Management costs for the jth oilfield project in the ith block
$ {s}_{ij} $	Cost of sales for the jth oilfield project in the ith block
$ {e}_{ij} $	Financial costs for the jth oilfield project in the ith block
$ \mathrm{N}\mathrm{P}\mathrm{V}\left(a\right) $	a discounted to the net present value in the year of decision
$ {C}_{1} $	Lower limit of operating cash flow per unit of production for inventory production
$ {C}_{2} $	Lower bound on operating costs per unit of production
$ {C}_{3} $	Lower bound on payout costs
$ {C}_{4} $	Lower bound on return on investment in new production
$ {C}_{5} $	Lower bound on total profit
$ {C}_{6} $	Lower limit of total production
$ {C}_{7} $	Total investment cap

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表 2 效益產量建模用目標函數

Table 2. Objective function used in benefit yield modeling

Objective function	Function formula	Sequence number
Yield maximization	$\mathrm{M}\mathrm{a}\mathrm{x}\displaystyle\sum _{i=1}^{m}\displaystyle\sum _{j=1}^{n}{x}_{ij}{q}_{ij}$	（1）
Minimize yield differential	$\mathrm{Min}\left(\right\|{Q}_{\mathrm{t}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{t} }-\mathrm{M}\mathrm{a}\mathrm{x}\displaystyle\sum _{i=1}^{m}\displaystyle\sum _{j=1}^{n}{x}_{ij}{q}_{ij}\left\|\right)$	（1*）
Profit maximization	$\mathrm{Max}\displaystyle\sum _{i=1}^{m}\displaystyle\sum _{j=1}^{n}{x}_{ij}{p}_{ij}$	（2）
Maximize cash flow	$\mathrm{Max}\displaystyle\sum _{i=1}^{m}\displaystyle\sum _{j=1}^{n}{x}_{ij}{c}_{ij}$	（3）
Minimize operating cost per unit	$ \mathrm{Min}\frac{\displaystyle\sum_{i=1}^{m}\displaystyle\sum_{j=1}^{n}{x}_{ij}{o}_{ij}}{\displaystyle\sum_{i=1}^{m}\displaystyle\sum_{j=1}^{n}{x}_{ij}{q}_{ij}} $	（4）
Minimize unit cash cost	$ \mathrm{Min}\frac{\displaystyle\sum_{i=1}^{m}\displaystyle\sum_{j=1}^{n}{x}_{ij}({o}_{ij}+{m}_{ij}+{s}_{ij}+{e}_{ij})}{\displaystyle\sum_{i=1}^{m}\displaystyle\sum_{j=1}^{n}{x}_{ij}{q}_{ij}} $	（5）
Minimize risk	$\mathrm{Min}\mathrm{M}\mathrm{a}\mathrm{x}\displaystyle\sum _{i=1}^{m}\displaystyle\sum _{j=1}^{n}{x}_{ij}{r}_{ij}$	（6）

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表 3 效益產量建模用約束條件

Table 3. Constraint conditions for benefit yield modeling

Constraint	Function formula	Sequence number
Lower limit of operating cash flow per unit of production	$\displaystyle\sum _{i=1}^{m}\displaystyle\sum _{j=1}^{n}{x}_{ij}({c}_{i}+{i}_{i})\geqslant{C}_{1}$	（7）
Upper limit of operating cost per unit of production	$ \frac{\displaystyle\sum_{i=1}^{m}\displaystyle\sum_{j=1}^{n}{x}_{ij}{o}_{ij}}{\displaystyle\sum_{i=1}^{m}\displaystyle\sum_{j=1}^{n}{x}_{ij}{q}_{ij}}\leqslant{C}_{2} $	（8）
Upper limit of payout cost	$\frac{\displaystyle\sum _{i=1}^{m}\displaystyle\sum _{j=1}^{n}{x}_{ij}({o}_{ij}+{m}_{ij}+{s}_{ij}+{e}_{ij})}{ \displaystyle\sum _{i=1}^{m}\displaystyle\sum _{j=1}^{n}{x}_{ij}{q}_{ij} }\leqslant{C}_{3}$	（9）
Lower limit of return on investment for new production volume	$ \frac{\displaystyle\sum_{i=1}^{m}\displaystyle\sum_{j=1}^{n}\displaystyle\sum_{t=2021}^{T}{x}_{ij}NPV\left({c}_{ij}^{t}\right)}{\displaystyle\sum_{i=1}^{m}\displaystyle\sum_{j=1}^{n}\displaystyle\sum_{t=2021}^{T}{x}_{ij}NPV\left({i}_{ij}^{t}\right)}\geqslant{C}_{4} $	（10）
Lower limit of total profit	$\displaystyle\sum _{i=1}^{m}\displaystyle\sum _{j=1}^{n}{x}_{ij}{p}_{ij}\geqslant{C}_{5}$	（11）
Lower limit of total production	$\displaystyle\sum _{i=1}^{m}\displaystyle\sum _{j=1}^{n}{x}_{ij}{q}_{ij}\geqslant{C}_{6}$	（12）
Total investment upper limit	$\displaystyle\sum _{i=1}^{m}\displaystyle\sum _{j=1}^{n}{x}_{ij}{i}_{ij}\leqslant{C}_{7}$	（13）
Required constraint-block constraint	$\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{ }\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}i,\displaystyle\sum _{j=1}^{n}{x}_{ij}\geqslant n$	（14）

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表 4 效益產量決策優化常用模型及表達式組合

Table 4. Common models and expression combinations for benefit yield decision optimization

Decision optimization model
Type	Expression Combination
Model I: Maximize the production and minimize the risk given the ROI constraints, cash flow, and the range of profits achieved	Objective function：(1), (5) Constraint：(7), (10), (11), (14)
Model II: Maximize the profit and cash flow and minimize the unit operating costs given the ROI constraints, payout costs, and range of production	Objective function：(2), (3), (4) Constraint：(9), (10), (12), (14)
Model III: Given the yield target $ {Q}_{\mathrm{t}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{t}} $ and investment constraints, profit, and unit operating cost requirements, minimize yield deviation and risk	Objective function：(1*)(5) Constraint：(8), (11), (13), (14)
Notes:ROI means return on investment.

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表 5 效益產量最優決策區間

Table 5. Decision intervals for benefit yield optimization

Preferred solution set	Equity oil and gas production/t	Profit/￥	Operating cash flow/￥	Risk	Unit operating costs/ (￥·t^?1)	Unit cash paid cost/ (￥·t^?1)	Total number of oil fields
1	37310841	4009049828	7094331796	20.4	436.8	504.0	36
2	37258035	3968636467	7806138825	16.8	441.0	508.2	21
3	37341255	3963712429	7399291824	18.8	441.0	508.2	29
4	36716835	3290670240	9689161738	14.1	449.4	516.6	9
5	37502802	3513549687	7006716908	21.3	441.0	508.2	39
6	36723054	3804018854	9127457671	16.4	441.0	508.2	19
7	36605850	3693330144	9536101806	13.8	449.4	516.6	9
8	36881063	3877670734	8692999494	17.2	441.0	508.2	23
9	36829709	3782262111	9512866333	15.2	445.2	512.4	14
10	37012710	3961423106	7745694098	17.1	441.0	508.2	22

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