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High investment highlights the importance of optimizing gathering pipeline networks. In view of the problem of ignoring hydraulic characteristics in previous studies, an integrated mixed-integer linear programming (MILP) model for gathering pipeline networks is developed in this study. Taking the minimum total construction cost as the objective function, the proposed model considers the economic and technical constraints such as obstacles, three-dimensional terrain, pipeline topological structures, pipe diameters, wellhead back pressure, and pressure equipment. The ant colony optimization algorithm is used for route optimization to provide parameters for the proposed model. Moreover, a piecewise method is employed to linearize the nonlinear hydraulic equations. Then, the model is solved by the CPLEX solver to obtain the optimal solution integrally, including the optimal connection topology, the location of the central processing facility, the position of pressure equipment, the diameter and detailed route of each pipeline, the pipeline flowrate and the node pressure. Finally, two real-world gas fields and a virtual oil field are taken as examples to demonstrate the feasibility and practicality of the model. The optimal results illustrate that this model can be implemented as a decision-support tool to optimize gathering pipeline networks in the actual design process.

Onshore gas field gathering pipeline system (GPS) plays a key role in the onshore gas field production and is often constructed in stages due to the phased development of the gas field. However, the influence of phased development on the optimal design of GPS has been neglected in previous studies. Although some research on the optimization of gas field development strategy involves the multi-period construction of GPS, they simplify the characteristics of the gathering network and ignore some very important parameters. This study develops an integrated mixed-integer linear programming (MILP) model for optimizing multi-period GPS to determine the central processing facility (CPF) location, pipeline (routes and diameters) installation and expansions, well site-CPF connections, the flowrate of each pipeline, and the operating pressure of each node in each time period simultaneously. Taking minimum total construction cost as the objective function, the proposed model considers various operational and technical constraints related to multi-period construction and hydraulic characteristics, such as obstacles, three-dimensional terrain, pipeline topological structures, pipeline diameters, and wellhead pressure. Ant colony optimization is used for route optimization to provide parameters for the proposed model. A piecewise approximation method is employed to deal with the nonlinear terms of hydraulic equations. Therefore, the MILP model can be solved by the branch-and-bound algorithm to obtain the global optimal solution integrally. Finally, the model is successfully applied to three real-world gas fields. Compared with the actual construction scheme and other literature methods, the results prove the superiority of multi-period planning considering time.

Compared with the conventional method utilizing permanent processing facilities, modular infrastructure has shown greater potential to hedge against the uncertainty due to the rapidly declining characteristic of shale gas production. However, there is still a lack of a comprehensive approach that integrates the production planning and the dynamic allocation of the modular infrastructure based on the production curve of shale gas wells. Therefore, a systematic optimization framework is developed to simultaneously optimize the production planning and modular infrastructure allocation over a given time horizon, maximizing the net present value (NPV) of the system, considering the production curve and gas well status, the processing capacity as well as the scheduling of modular infrastructure. The proposed mixed-integer linear programming model combines the decisions relevant to shale gas production, transportation, and processing together with the decisions regarding modular infrastructure, including allocation, capacity selection, installment planning, moving scheduling, and salvage operation. A case study deciding a development strategy for 24 multi-well pads over a period of 15 years is implemented to illustrate the applicability of the proposed model. The results show that the dynamic allocation of the modular infrastructure can adapt to productivity fluctuations. The use of the modular approach increases the NPV by 9.12% and has a high utilization efficiency of processing devices compared to the conventional method. This work reveals that the synergistic interaction of the production planning and modular infrastructure dynamic allocation can increase efficiencies in the uses of energy, resources, and human capital to promote cleaner production practices.

Gathering pipeline network, as one of the most important parts of oil and gas field construction, is characterized by complex topological structure and high investment costs. Therefore, optimization of gathering pipeline network can reduce total investment of field significantly. This paper focuses on stellated pipeline network, a common connection structure of gathering pipeline, and presents a layout optimization model for progressive development of gathering pipeline network. Minimizing the production length is the object of the model. Constraints of wells, valve groups, gathering radius and flow rate are taken into consideration. The optimal position of valve groups and central processing facility and the connection of each pipeline are obtained by solving this model with a heuristic algorithm proposed in this paper. In addition, three typical cases, namely new production block, well pattern infilling and rolling development, are taken as examples to verify the reliability and practicality of the proposed model. The results demonstrate that the proposed method can tackle the layout optimization problem of expanding a pre-existing gathering pipeline network.

Gathering network, which is usually characterized by various and complex structure, takes a large proportion of the overall construction cost of gas field. Optimization of pipeline routes is an effective way to reduce the investment. In this paper, a novel model for optimal route of pipeline considering complex terrains and obstacles is proposed and solved by Genetic Algorithm. Minimizing the total investment is the object of this model. Since the construction costs under different terrains are different, the distance factor Li, slope factor Di and elastic factor Si are introduced into the objective function to represent the length of the pipeline, the gradient of the pipeline, and the fluctuation of terrain. In addition, the performance of the model is verified by taking three typical situations of different terrains and obstacles as examples. The results illustrate that the proposed model can address the optimal design of pipeline routes in complex terrains. Moreover, the effects of different genetic operators on solutions are investigated, including three selection operators and two crossover operators. The study provides a guideline for designing pipeline routes in complex terrains and is also applicable to the analogous problem.

Underground gas storage plays a significant role in ensuring gas supply reliability of natural gas pipeline systems. However, the specific hydraulic characteristics of the underground gas storage and the uncertainties in its gas injection/production capacity are generally overlooked when evaluating the gas supply reliability. Therefore, an integrated methodology to assess gas supply reliability of the natural gas pipeline system is developed in this study, and three aspects of uncertainty and hydraulic characteristic of the natural gas pipeline system are both considered. Based on system’s gas supply strategy, the amounts of gas supplied by the transmission pipeline system and required by the consumers are calculated firstly. The underground gas storage is then employed to regulate the supply-demand imbalance between the transmission pipeline system and market demand by performing its gas injection/production function. Moreover, the operational reliability of the underground gas storage is evaluated to determine whether it is able to complete the specified gas injection/production task. Then, the total daily amount of gas supplied to the consumers, is obtained, and two indicators proposed to quantify the gas supply reliability are then calculated. Finally, the expected gas supply reliability is assessed based on a large number of Monte Carlo trials. Moreover, the methodology is applied to a simplified gas pipeline system to confirm its feasibility, and system’s ability to satisfy the consumers demand is evaluated, and the weakest consumer node is identified. Furthermore, the gas supply reliability is overestimated without considering the uncertainties in the underground gas storage’s gas injection/production capacity.