scholarly journals Route Optimization of Pipeline in Gas-Liquid Two-Phase Flow Based on Genetic Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Zhou ◽  
Guangchuan Liang ◽  
Tao Deng ◽  
Jing Gong

This paper describes the problems in route optimization of two-phase pipelines. Combining the hydraulic calculation with route optimization theory, this paper establishes an automatic route optimization model and adopts the general genetic algorithm (gGA) and steady-state genetic algorithm (ssGA) to solve the model, respectively, gets the optimal route, and discusses the influence of parameters setting to the result. This algorithm was applied in determining pipelines routes in coalbed methane gathering and transporting system in Shanxi Province, China. The result shows that the algorithm is feasible, which improves the hydraulic properties by reducing the pressure drop along the line while the pipeline length is still acceptable.

Fuel ◽  
2018 ◽  
Vol 222 ◽  
pp. 193-206 ◽  
Author(s):  
Fansheng Huang ◽  
Yili Kang ◽  
Lijun You ◽  
Xiangchen Li ◽  
Zhenjiang You

SPE Journal ◽  
2018 ◽  
Vol 24 (02) ◽  
pp. 681-697 ◽  
Author(s):  
Zheng Sun ◽  
Juntai Shi ◽  
Keliu Wu ◽  
Tao Zhang ◽  
Dong Feng ◽  
...  

Summary Low-permeability coalbed-methane (CBM) reservoirs possess unique pressure-propagation behavior, which can be classified further as the expansion characteristics of the drainage area and the desorption area [i.e., a formation in which the pressure is lower than the initial formation pressure and critical-desorption pressure (CDP), respectively]. Inevitably, several fluid-flow mechanisms will coexist in realistic coal seams at a certain production time, which is closely related to dynamic pressure and saturation distribution. To the best of our knowledge, a production-prediction model for CBM wells considering pressure-propagation behavior is still lacking. The objective of this work is to perform extensive investigations into the effect of pressure-propagation behavior on the gas-production performance of CBM wells. First, the pressure-squared approach is used to describe the pressure profile in the desorption area, which has been clarified as an effective-approximation method. Also, the pressure/saturation relationship that was developed in our previous research is used; therefore, saturation distribution can be obtained. Second, an efficient iteration algorithm is established to predict gas-production performance by combining a new gas-phase-productivity equation and a material-balance equation. Finally, using the proposed prediction model, we shed light on the optimization method for production strategy regarding the entire production life of CBM wells. Results show that the decrease rate of bottomhole pressure (BHP) should be slow at the water single-phase-flow stage, fast at the early gas/water two-phase-flow stage, and slow at the late gas/water two-phase-flow stage, which is referred to as the slow/fast/slow (SFS) control method. Remarkably, in the SFS control method, the decrease rate of the BHP at each period can be quantified on the basis of the proposed prediction model. To examine the applicability of the proposed SFS method, it is applied to an actual CBM well in Hancheng Field, China, and it enhances the cumulative gas production by a factor of approximately 1.65.


Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Tianran Ma ◽  
Hao Xu ◽  
Chaobin Guo ◽  
Xuehai Fu ◽  
Weiqun Liu ◽  
...  

As a complex two-phase flow in naturally fractured coal formations, the prediction and analysis of CBM production remain challenging. This study presents a discrete fracture approach to modeling coalbed methane (CBM) and water flow in fractured coal reservoirs, particularly the influence of fracture orientation, fracture density, gravity, and fracture skeleton on fluid transport. The discrete fracture model is first verified by two water-flooding cases with multi- and single-fracture configurations. The verified model is then used to simulate CBM production from a discrete fractured reservoir using four different fracture patterns. The results indicate that fluid behavior is significantly affected by orientation, density, and fracture connectivity. Finally, several cases are performed to investigate the influence of gravity and fracture skeleton. The simulation results show that gas migrates upwards to the top reservoir during fluid extraction owing to buoyancy and the connected fracture skeleton plays a dominant role in fluid transport and methane production efficiency. Overall, the developed discrete fracture model provides a powerful tool to study two-phase flow in fractured coal reservoirs.


2016 ◽  
Vol 33 ◽  
pp. 324-336 ◽  
Author(s):  
Sheng Li ◽  
Chaojun Fan ◽  
Jun Han ◽  
Mingkun Luo ◽  
Zhenhua Yang ◽  
...  

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1151
Author(s):  
Yanbao Liu ◽  
Zhigang Zhang ◽  
Wei Xiong ◽  
Kai Shen ◽  
Quanbin Ba

The increasing demand on coal production has led to the gradually increase of mining depth and more high methane mines, which bring difficulties in terms of coalbed methane (CBM) extraction. Hydraulic fracturing is widely applied to improve the production of CBM, control mine gas, and prevent gas outbursts. It improves coal bed permeability and accelerate desorption and migration of CBM. Even though the impacts of hydraulic fracturing treatment on the coal reservoirs are rare, negative effects could not be totally ignored. To defend this defect, the presented work aims to study the influence of water filtration on coal body deformation and permeability evolution. For this purpose, a simulation based finite element method was developed to build a solid-fluid coupled two-phase flow model using commercial software (COMSOL Multiphysics 5.4). The model was verified using production data from a long strike borehole from Wangpo coal mine in Shanxi Province, China. Several simulation scenarios were designed to investigate the adverse impacts of hydraulic fracturing on gas flow behaviors. The mechanisms of both relative and intrinsic permeability evolutions were analyzed, and simulation results were presented. Results show that the intrinsic permeability of the fracture system increases in the water injection process. The impacts of water imitation were addressed that a critical time was observed beyond which water cannot go further and also a critical pressure exists above which the hydraulic pressure would impair the gas flow. Sensitivity analysis also showed that a suitable time and pressure combination could be observed to maximize gas extraction. This work provides an efficient approach to guide the coal bed methane exploitation and other unconventional gas reservoirs.


2021 ◽  
Author(s):  
Subrata Bhowmik

Abstract Optimal route selection for the subsea pipeline is a critical task for the pipeline design process, and the route selected can significantly affect the overall project cost. Therefore, it is necessary to design the routes to be economical and safe. On-bottom stability (OBS) and fixed obstacles like existing crossings and free spans are the main factors that affect the route selection. This article proposes a novel hybrid optimization method based on a typical Machine Learning algorithm for designing an optimal pipeline route. The proposed optimal route design is compared with one of the popular multi-objective optimization method named Genetic Algorithm (GA). The proposed pipeline route selection method uses a Reinforcement Learning (RL) algorithm, a particular type of machine learning method to train a pipeline system that would optimize the route selection of subsea pipelines. The route optimization tool evaluates each possible route by incorporating Onbottom stability criteria based on DNVGL-ST-109 standard and other constraints such as the minimum pipeline route length, static obstacles, pipeline crossings, and free-span section length. The cost function in the optimization method simultaneously handles the minimization of length and cost of mitigating procedures. Genetic Algorithm, a well established optimization method, has been used as a reference to compare the optimal route with the result from the proposed Reinforcement Learning based optimization method. Three different case studies are performed for finding the optimal route selection using the Reinforcement Learning (RL) approach considering the OBS criteria into its cost function and compared with the Genetic Algorithm (GA). The RL method saves upto 20% pipeline length for a complex problem with 15 crossings and 31 free spans. The RL optimization method provides the optimal routes, considering different aspects of the design and the costs associated with the various factors to stabilize a pipeline (mattress, trenching, burying, concrete coating, or even employing a more massive pipe with additional steel wall thickness). OBS criteria significantly influence the best route, indicating that the tool can reduce the pipeline's design time and minimize installation and operational costs of the pipeline. Conventionally the pipeline route optimization is performed by a manual process where the minimum roule length and static obstacles are considered to find an optimum route. The engineering is then performed to fulfill the criteria of this route, and this approach may not lead to an optimized engineering cost. The proposed Reinforced Learning method for route optimization is a mixed type, faster, and cost-efficient approach. It significantly minimizes the pipeline's installation and operational costs up to 20% of the conventional route selection process.


1971 ◽  
Vol 11 (03) ◽  
pp. 215-222 ◽  
Author(s):  
Uri Shamir

Abstract A technique is described, which makes it possible to select the optimal route for a pipeline designed to carry oil and gas in two-phase flow. The pipeline is assumed to operate under the pressure differential naturally available between the source and the point of delivery. point of delivery. A discrete grid is established to describe the corridor through which the pipeline is to pass. Topograpbic and terrain data are given for all grid points. Cost data is given for all factors which points. Cost data is given for all factors which affect the capital cost of the pipeline. The equation for the two-phase flow becomes a global constraint, to be satisfied by the selected route. Dynamic programming is then used to solve the minimization programming is then used to solve the minimization problem. problem. A computer program is described, with which a sample problem was solved, and the results that were obtained are also presented. Introduction Great sums of money are spent annually on the construction of pipelines for the oil industry. Many of these pipelines are designed to carry gas and oil in simultaneous two-phase flow from wells to various collecting and processing facilities. The procedures for selecting the route for such pipelines procedures for selecting the route for such pipelines have followed the traditional approach of engineering judgment and selection of the cheapest among a few alternative routes laid out by hand on maps and aerial photos. Aerial photo interpretation, to yield soil types, tree cover, existence of swamps and muskeg, and other factors affecting costs, is being used in route selection. Geologists and soil engineers are brought in to evaluate soil conditions on the basis of aerial photos, as well as by examination of the route itself and soil samples. This data is then used to select a route and to design the pipeline. The present project was undertaken with the objective of improving the engineering practice. We sought to proceed beyond the stage of mere trial and error and to develop a rigorous method for determining the optimal route by using the techniques of systems engineering. The over-all problem of conveying fluids in one-and two-phase flow pipelines was reviewed. It ranges from a single pipe carrying a single-phase fluid, through two-phase flow lines, to gathering systems containing networks of pipes and other equipment, such as valves and compressors, to collect the products of a large number of wells and deliver the mixed product to processing plants. All these were considered part of the over-all project, which deals with optimal design of pipeline systems. Initially, one aspect of the over-all project had to be selected. It was decided to tackle the problem of optimizing the route for a single pipeline carrying two-phase flow. This problem presents some complications, and it was felt that if it could be solved, single-phase pipelines would present no added difficulties. TWO-PHASE FLOW PIPELINES It is common practice in the oil industry to use a single pipe to carry both oil and gas from producing wells to collecting facilities and plants. The alternative is to separate the two phases at the source and carry them in separate pipelines. Economics of the two alternatives should be the basis for a choice between them. The present work is therefore a useful tool for making a better choice possible by yielding the optimal solution for the possible by yielding the optimal solution for the two-phase line alternative. As will be shown later, the method, as well as the computer programs, can also be used to determine the optimal route for a pipeline carrying flow of a single fluid. pipeline carrying flow of a single fluid. COMPUTING SIMULTANEOUS FLOW OF LIQUID AND GAS IN A PIPELINE The regime of flow in a pipeline carrying both liquid and gas depends on many parameters. The regime, in turn, determines the pressure losses along the pipeline. The procedures for computing the two-phase flow are both elaborate and rather inaccurate. No attempt is made in the present work to change or to improve the existing methods, as this is beyond its scope. We do need, however, to modify the sequence of the computations to suit the requirements of the optimization problem. SPEJ P. 215


Sign in / Sign up

Export Citation Format

Share Document