Optimizing the Steady Operation of an Double Oil Pipeline System by Decompostion Method

2004 ◽  
Author(s):  
Changchun Wu ◽  
Guotai Shao
Keyword(s):  
Crude Oil ◽  
Programming Problem ◽  
Large Scale ◽  
Optimization Problem ◽  
Petroleum Industry ◽  
Pipeline System ◽  
Long Distance ◽  
Oil Pipeline ◽  
Discharge Temperature ◽  
Decision Variables

As a main channel for long distance transportation of Daqing crude oil, Daqing-Tieling oil pipeline system consists of two pipelines in parallel. With its capacity of 45 million tons per year, the system is the largest oil pipeline system in China and plays an important role in the petroleum industry and national economy of China. Due to the complicated interconnection between the two pipelines in the system, the optimization of steady operation of the system is much more difficult than a single pipeline so that it can be considered as an optimization problem on large scale system. Besides the interconnection of the two pipelines, because of high pour point of Daqing crude oil, another difficulty to solve the problem comes from the fact that the two pipelines are hot oil pipeline, of which the heating-pumping stations are equipped with some heaters to heat the crude oil so as to improve its flow ability. For the optimization problem, the basic decision variables can be divided into two types, the discharge temperature of each heating-pumping station and the 0–1 variable which assigns a pump online or offline, and they are dependent to each other. Under certain conditions, the problem can be decomposed into two relatively independent sub-problems, one being the optimization of the oil temperatures in the system, another being the optimization of the matching between a pump combination and the all pipe segments of the system. The first sub-problem has been modeled as a nonlinear programming problem with 55 decision variables and more than one hundred constraints. For simplifying the solving process of the sub-problem, it has been further decomposed into a set of sub-problems, again, each of which can be easily solved. The second sub-problem can be modeled as a dynamic programming problem. On the basis of the models and the algorithms proposed for the above-mentioned problem, a software QTOPT has been developed specially for the Daqing-Tieling oil pipeline system, and has been used in evaluating and optimizing the process design of the system. Also the software can be used to optimize the steady operation of the system.

scholarly journals Drag Reducer Selection for Oil Pipeline Based Laboratory Experiment

2017 ◽  
Vol 12 (1) ◽  
pp. 112 ◽  
Author(s):  
Leksono Mucharam ◽  
Silvya Rahmawati ◽  
Rizki Ramadhani
Keyword(s):  
Crude Oil ◽  
Large Scale ◽  
Oil And Gas ◽  
Energy Requirement ◽  
Pipeline System ◽  
Chemical Application ◽  
Oil Pipeline ◽  
Pipeline Flow ◽  
Oil Transportation ◽  
Pump Stations

Oil and gas industry is one of the most capital-intensive industry in the world. Each step of oil and gas processing starting from exploration, exploitation, up to abandonment of the field, consumes large amount of capital. Optimization in each step of process is essential to reduce expenditure. In this paper, optimization of fluid flow in pipeline during oil transportation will be observed and studied in order to increase pipeline flow performance.This paper concentrates on chemical application into pipeline therefore the chemical can increase overall pipeline throughput or decrease energy requirement for oil transportation. These chemicals are called drag reducing agent, which consist of various chemicals such as surfactants, polymers, nanofluids, fibers, etc. During the application of chemical into pipeline flow system, these chemicals are already proven to decrease pump work for constant flow rate or allow pipeline to transport more oil for same amount of pump work. The first application of drag reducer in large scale oil transportation was in Trans Alaskan Pipeline System which cancel the need to build several pump stations because of the successful application. Since then, more company worldwide started to apply drag reducer to their pipeline system.Several tedious testings on laboratory should be done to examine the effect of drag reducer to crude oil that will be the subject of application. In this paper, one of the testing method is studied and experimented to select the most effective DRA from several proposed additives. For given pipeline system and crude oil type, the most optimum DRA is DRA A for pipeline section S-R and for section R-P is DRA B. Different type of oil and pipeline geometry will require different chemical drag reducer. 

scholarly journals Comparison of data mining algorithms for pressure prediction of crude oil pipeline to identify congeal

2021 ◽  
Vol 325 ◽  
pp. 02002
Author(s):  
Agus Santoso ◽  
F. Danang Wijaya ◽  
Noor Akhmad Setiawan ◽  
Joko Waluyo
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Crude Oil ◽  
Petroleum Industry ◽  
Pipeline System ◽  
Data Mining Algorithm ◽  
Oil Pipeline ◽  
Data Mining Algorithms ◽  
Mining Algorithm ◽  
Mining Algorithms

Data mining is applied in many areas. In oil and gas industries, data mining may be implemented to support the decision making in their operation to prevent a massive loss. One of serious problems in the petroleum industry is congeal phenomenon, since it leads to block crude oil flow during transport in a pipeline system. In the crude oil pipeline system, pressure online monitoring in the pipeline is usually implemented to control the congeal phenomenon. However, this system is not able to predict the pipeline pressure on the next several days. This research is purposed to compare the pressure prediction of the crude oil pipeline using data mining algorithms based on the real historical data from the petroleum field. To find the best algorithms, it was compared 4 data mining algorithms, i.e. Random Forest, Multilayer Perceptron (MLP), Decision Tree, and Linear Regression. As a result, the Linear Regression shows the best performance among the 4 algorithms with R2 = 0.55 and RMSE = 28.34. This research confirmed that data mining algorithm is a good method to be implemented in petroleum industry to predict the pressure of the crude oil pipeline, even the accuracy of the prediction values should be improved. To have better accuracy, it is necessary to collect more data and find better performance of the data mining algorithm

scholarly journals Safety Study on Wax Deposition in Crude Oil Pipeline

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1572
Author(s):  
Bin Yao ◽  
Deyin Zhao ◽  
Zhi Zhang ◽  
Cheng Huang
Keyword(s):  
Crude Oil ◽  
Cloud Point ◽  
Freezing Point ◽  
Development Trend ◽  
Wax Deposition ◽  
Scanning Calorimetry ◽  
Long Distance ◽  
Wax Precipitation ◽  
Oil Pipeline ◽  
Precipitation Characteristics

The Shunbei crude oil pipeline is prepared to use the unheated transportation process to transport waxy crudes. However, the wax formation in the pipeline is unknown. In order to predict the wax deposition of the pipeline, the physical property experiment of Shunbei crude oil was carried out through field sampling. The density, freezing point, hydrocarbon composition, and viscosity–temperature characteristics of crude oil are obtained. The cloud point and wax precipitation characteristics of the crude oil were obtained using the differential scanning calorimetry (DSC) thermal analysis method. Then, the wax deposition rate of the pipeline was predicted by two methods: OLGA software and wax deposition kinetic model. Finally, the optimal pigging cycle of the pipeline was calculated on this basis. The results show that: Shunbei crude oil is a light crude oil with low wax content, a low freezing point, and a high cloud point. Comparing the OLGA simulation results with the calculation results of the Huang Qiyu model, the development trend of wax deposition along the pipeline was the same under different working conditions. The relative error of the maximum wax layer thickness was 6%, proving that it is feasible for OLGA to simulate wax deposition in long-distance crude oil pipelines. Affected by the wax precipitation characteristics of Shunbei crude oil, there was a peak of wax precipitation between the pipeline section where crude oil temperature was 9.31–13.31 °C and the recommended pigging cycle at the lowest throughput was 34 days in winter and 51 days in spring and autumn.

Oil Temperature Prediction of Long-Distance Hot-Crude-Oil Pipeline Based on GA-BP Optimization

2020 ◽  
Author(s):  
Tao Yu ◽  
Peng Dong ◽  
Yang Yu ◽  
Jinzhou Song ◽  
Jie Zhang
Keyword(s):  
Prediction Model ◽  
Crude Oil ◽  
Pour Point ◽  
Theoretical Formula ◽  
Production Data ◽  
Influence Coefficient ◽  
Long Distance ◽  
Temperature Prediction ◽  
Oil Pipeline ◽  
Actual Production

Abstract Due to the high pour point of the oil products transported in the long-distance high wax crude oil pipeline, in order to ensure the operation safety, it is necessary to adopt heating transmission technology, so as to ensure that the oil temperature along the pipeline is 3–5 °C higher than the pour point, that is to say, the oil temperature is the most important operation parameter of the long-distance hot oil pipeline, and the accurate prediction and control of the oil temperature is the premise of the pipeline safety optimization. Aiming at the problems of large prediction error and poor applicability of the previous theoretical formula, this paper studies the establishment of oil temperature prediction model by using data mining algorithms such as Back Propagation (BP) neural network, and improves the prediction efficiency and accuracy of the model by using Genetic Algorithm (GA) optimization. The correlation coefficient formula is used to calculate the influence coefficient of oil temperature, ground temperature, pipeline transportation and other parameters on the inlet oil temperature of the downstream station, so as to obtain the input parameters of the model. The actual production data training model is downloaded through SCADA system, and the prediction accuracy of the control model is ±0.5 °C. Compared with BP model and other theoretical formulas, the accuracy and efficiency of GA-BP oil temperature prediction model are greatly improved, and the adaptability is better. The GA-BP oil temperature prediction model trained according to the actual production data can be effectively applied to the future pipeline big data platform, which lays a theoretical foundation for the intelligent control of the pipeline.

Conversion of Old Crude Oil Pipeline to Product Service

2004 ◽  
Author(s):  
Subhash Chandra Agarwal
Keyword(s):  
Crude Oil ◽  
Capital Investment ◽  
Pipeline System ◽  
Western India ◽  
Environment Friendly ◽  
Oil Pipeline ◽  
Product Service ◽  
Products Pipeline ◽  
Oil Company ◽  
System Methodology

Due to capacity expansion of one of our refineries located in Western India, there was a need to evacuate additional products. Pipeline, being the most economical, reliable and environment friendly mode of transportation was the obvious choice. Laying a new pipeline would have required making substantial initial capital investment. However, a crude oil pipeline, owned by another oil company, was terminating at the refinery and was not in regular use. It was decided to convert this pipeline to product service. The pipeline was taken on lease, extensively cleaned, tested and successfully converted to product service with necessary hook-up/modifications at both the ends and in-between. The paper covers the experience gathered during the process of conversion of the crude oil pipeline to product service, including modifications carried out in the pipeline system, methodology adopted for cleaning, hydro-testing and commissioning of the system, and the lessons learnt.

Analysis of Hot Oil Pipeline Stress Influencing Factors

2014 ◽  
Vol 887-888 ◽  
pp. 899-902
Author(s):  
Xiao Nan Wu ◽  
Shi Juan Wu ◽  
Hong Fang Lu ◽  
Jie Wan ◽  
Jia Li Liu ◽  
...  
Keyword(s):  
Crude Oil ◽  
High Viscosity ◽  
Analysis Model ◽  
Long Distance ◽  
Oil Pipeline ◽  
Software Analysis ◽  
Key Points ◽  
Temperature And Pressure ◽  
Pressure Changes ◽  
The Impact

In order to reduce the viscosity of crude oil for transport, we often use the way of heating delivery for high pour point, high wax, and high viscosity oil. Crude oil at high temperature, through long-distance transmission, the temperature and pressure changes on the piping stress greater impact. In this paper, in order to explore the main factor of hot oil pipeline stress and the location of key points, we build the XX hot oil pipeline stress analysis model used CAESAR II software, analysis of the impact of changes in temperature and pressure on piping stress when hot oil pipeline running, draw hot oil pipeline stress distribution, clearly identifies the location of key points of stress concentration, and we have come to that temperature is a major factor in generating pipe stress.

Dynamics of a Large Scale Hydraulic Capsule Pipeline System

1999 ◽  
Vol 124 (1) ◽  
pp. 191-195 ◽  
Author(s):  
Hongliu Du ◽  
Satish S. Nair
Keyword(s):  
Large Scale ◽  
Control Method ◽  
Experimental Studies ◽  
Small Scale ◽  
Pipeline System ◽  
Centrifugal Pumps ◽  
Long Distance ◽  
Column Separation ◽  
In Series ◽  
Hydraulic Capsule Pipeline

The dynamics of a booster station, which is critical for the control of a novel, long distance, hydraulic capsule pipeline, is simulated mathematically for design studies and control of the hydraulic transients caused by the valve actuators in the system. Several modifications to the pump bypass station configuration of the booster station have been studied. With the objective of eliminating column separation and reducing flow reversals, a configuration with several centrifugal pumps connected in series, and a carefully sized air chamber is found to be a viable design. A valve control method is designed to eliminate column separation and the design results in acceptable flow reversal levels in the main pipe. The simulation results match with trends in limited experimental studies performed on a small scale experimental capsule pipeline system.

scholarly journals Computer-based method of design and modeling of transient flow in crude oil pipeline system

2020 ◽  
Vol 8 (3) ◽  
pp. 219-239
Author(s):  
ThankGod Enatimi Boye ◽  
◽  
Olusegun David Samuel ◽  
Keyword(s):  
Crude Oil ◽  
Transient Flow ◽  
Pipeline System ◽  
Oil Pipeline ◽  
Computer Based ◽  
Method Of Design

High Speed Data Communications and High Speed Leak Detection Models: Impact of Thermodynamic Properties for Heated Crude Oil in Large Diameter, Insulated Pipelines — Application Pacific Pipeline System

2000 ◽  
Author(s):  
Travis Mecham ◽  
Galen Stanley ◽  
Michael Pelletier ◽  
Jim C. P. Liou
Keyword(s):  
Physical Properties ◽  
Crude Oil ◽  
High Speed ◽  
Detection System ◽  
Large Diameter ◽  
Leak Detection ◽  
San Joaquin Valley ◽  
Pipeline System ◽  
Oil Pipeline ◽  
Detection Model

Recent advances in SCADA and leak detection system technologies lead to higher scan rates and faster model speeds. As these model speeds increase and the inherent mathematical uncertainties in implicit method solutions are reduced, errors and uncertainties in measurement of the physical properties of the fluids transported by pipeline come to dominate the confidence calculations for computer generated leak alerts in the control center. The ability to collect more data must be supported by the need for better model data in order to achieve optimal leak detection system performance. This is particularly true when the products transported are non-homogeneous and have strong viscosity-vs-temperature relationships. These are characteristics of crude oils in California’s San Joaquin Valley where significant heating is required to pump these oils in an efficient manner. Proper characterization and correct mathematical expression of these physical properties in leak models has become critical. This paper presents these new developments in the context of an implementation of this new technology for the Pacific Pipeline System (PPS). PPS is a recently constructed and commissioned 209 km (130-mile), 50.8 cm (20″) diameter, insulated, hot crude oil pipeline between the southern portion of California’s San Joaquin Valley and refineries in the Los Angeles basin. Operational temperatures in this line vary from ambient to 82.2°C (180°F) with pressures ranging from 345 kPa (50 psi) to 11,720 kPa (1700 psi). Due to the unique geometry of the line, facilities along the route include pumping stations, metering stations and numerous “throttle-type” pressure reduction facilities. On PPS, a high-speed leak detection model is supported by a fiber optic (OC-1) communication backbone with data rate capacities in excess of 50 Megabits Per Second (MPS). Total scan times for the distributed communication system have been reduced to 1/4 second — each facility reports data to the SCADA host four times each second. A corresponding 1/4 second leak detection model cycle leads to selection of Methods of Characteristics segments on the order of 260 meters (850 feet). This resolution, in conjunction with the advanced instrumentation package of PPS, makes detection of very small leaks realizable. This paper starts with an overview of the system and combines a mix of the theoretical requirements imposed by the mathematical solutions with a practical description of the laboratory procedures and propagated experimental errors. The paper reviews temperature-related errors and uncertainties and their influence on leak detection performance.

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