Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

oai:ojs.pkp.sfu.ca:article/2Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained from oil fields. Therefore, for training purposes hypothetical data are developed using the transmission pipeline model, by applying various physical configuration of pipeline and applying oil properties correlations to estimate the value of oil density and viscosity. The various leak locations and leak rates are also represented in this model. The prediction of those two leak parameters will be completed until the total error is less than certain value of tolerance, or until iterations level is reached. To recognize the pattern, forward procedure is conducted. The application of this approach produces conclusion that for certain pipeline network configuration, the higher number of iterations will produce accurate result. The number of iterations depend on the leakage rate, the smaller leakage rate, the higher number of iterations are required. The accuracy of this approach is clearly determined by the quality of training data. Therefore, in the preparation of training data the results of pressure drop calculations should be validated by the real measurement of pressure drop along the pipeline. For the accuracy purposes, there are possibility to change the pressure drop and fluid properties correlations, to get the better results. The results of this research are expected to give real contribution for giving an early detection of oil-spill in oil fields

Optimization of Vertical Well Placement for Oil Field Development Based on Basic Reservoir Rock Properties using a Genetic Algorithm

Comparing the quality of basic reservoir rock properties is a common practice  to  locate  new  infill  or  development  wells  for  optimizing  oil  field development using reservoir simulation. The conventional technique employs a manual  trial-and-error  process  to  find  new  well  locations,  which  proves  to  be time-consuming, especially for large fields. Concerning this practical matter, an alternative in the form of a robust technique is introduced in order to reduce time and  effort  in  finding  new  well  locations  capable  of  producing  the  highest  oil recovery. The objective of this research was to apply a genetic algorithm (GA) for determining well locations using reservoir simulation, in order to avoid the conventional  manual  trial-and-error  method.  This  GA  involved  the  basic  rock properties,  i.e.  porosity,  permeability,  and  oil  saturation,  of  each  grid  block obtained from a reservoir simulation model, to which a newly generated fitness function was applied, formulated by translating common engineering  practice in reservoir  simulation  into  a  mathematical  equation  and  then  into  a  computer program. The maximum fitness value indicates the best grid location for a new well. In order to validate the proposed GA method and evaluate the performance of the program, two fields with different production profile characteristics were used, fields X and Y. The proposed method proved to be a robust and accurate method to find the best new well locations for oil field development. The key to the success of the proposed  GA method lies in the formulation of the objective functions

Optimization of Vertical Well Placement for Oil Field Development Based on Basic Reservoir Rock Properties Using a Genetic Algorithm

Comparing the quality of basic reservoir rock properties is a common practice to locate new infill or development wells for optimizing oil field development using reservoir simulation. The conventional technique employs a manual trial-and-error process to find new well locations, which proves to be time-consuming, especially for large fields. Concerning this practical matter, an alternative in the form of a robust technique is introduced in order to reduce time and effort in finding new well locations capable of producing the highest oil recovery. The objective of this research was to apply a genetic algorithm (GA) for determining well locations using reservoir simulation, in order to avoid the conventional manual trial-and-error method. This GA involved the basic rock properties, i.e. porosity, permeability, and oil saturation, of each grid block obtained from a reservoir simulation model, to which a newly generated fitness function was applied, formulated by translating common engineering practice in reservoir simulation into a mathematical equation and then into a computer program. The maximum fitness value indicates the best grid location for a new well. In order to validate the proposed GA method and evaluate the performance of the program, two fields with different production profile characteristics were used, fields X and Y. The proposed method proved to be a robust and accurate method to find the best new well locations for oil field development. The key to the success of the proposed GA method lies in the formulation of the objective functions

Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained from oil fields. Therefore, for training purposes hypothetical data are developed using the transmission pipeline model, by applying various physical configuration of pipeline and applying oil properties correlations to estimate the value of oil density and viscosity. The various leak locations and leak rates are also represented in this model. The prediction of those two leak parameters will be completed until the total error is less than certain value of tolerance, or until iterations level is reached. To recognize the pattern, forward procedure is conducted. The application of this approach produces conclusion that for certain pipeline network configuration, the higher number of iterations will produce accurate result. The number of iterations depend on the leakage rate, the smaller leakage rate, the higher number of iterations are required. The accuracy of this approach is clearly determined by the quality of training data. Therefore, in the preparation of training data the results of pressure drop calculations should be validated by the real measurement of pressure drop along the pipeline. For the accuracy purposes, there are possibility to change the pressure drop and fluid properties correlations, to get the better results. The results of this research are expected to give real contribution for giving an early detection of oil-spill in oil fields

Optimization of Vertical Well Placement for Oil Field Development Based on Basic Reservoir Rock Properties using a Genetic Algorithm

Comparing the quality of basic reservoir rock properties is a common practice  to  locate  new  infill  or  development  wells  for  optimizing  oil  field development using reservoir simulation. The conventional technique employs a manual  trial-and-error  process  to  find  new  well  locations,  which  proves  to  be time-consuming, especially for large fields. Concerning this practical matter, an alternative in the form of a robust technique is introduced in order to reduce time and  effort  in  finding  new  well  locations  capable  of  producing  the  highest  oil recovery. The objective of this research was to apply a genetic algorithm (GA) for determining well locations using reservoir simulation, in order to avoid the conventional  manual  trial-and-error  method.  This  GA  involved  the  basic  rock properties,  i.e.  porosity,  permeability,  and  oil  saturation,  of  each  grid  block obtained from a reservoir simulation model, to which a newly generated fitness function was applied, formulated by translating common engineering  practice in reservoir  simulation  into  a  mathematical  equation  and  then  into  a  computer program. The maximum fitness value indicates the best grid location for a new well. In order to validate the proposed GA method and evaluate the performance of the program, two fields with different production profile characteristics were used, fields X and Y. The proposed method proved to be a robust and accurate method to find the best new well locations for oil field development. The key to the success of the proposed  GA method lies in the formulation of the objective functions

An Investigation on Gas Lift Performance Curve in an Oil-Producing Well

The main objective in oil production system using gas lift technique is to obtain the optimum gas injection rate which yields the maximum oil production rate. Relationship between gas injection rate and oil production rate is described by a continuous gas lift performance curve (GLPC). Obtaining the optimum gas injection rate is important because excessive gas injection will reduce production rate, and also increase the operation cost. In this paper, we discuss a mathematical model for gas lift technique and the characteristics of the GLPC for a production well, for which one phase (liquid) is flowing in the reservoir, and two phases (liquid and gas) in the tubing. It is shown that in certain physical condition the GLPC exists and is unique. Numerical computations indicate unimodal properties of the GLPC. It is also constructed here a numerical scheme based on genetic algorithm to compute the optimum oil production

A Mathematical Model of Intermittent Gas Lift in Elevation-Production Operation with Line-Pack and Line-Drafting Phenomena in a Gas Line

This paper discusses a transient model of the intermittent gas lift technique in an oil well. The model is developed in the gas line, in the tubing-casing annulus, and the tubing. The line-pack and line-drafting phenomena in the gas line are considered in the model. A numerical approach will be used to solve the mathematical model that represents fluid flow during intermittent gas lift injection. The dynamics of important variables in the intermittent gas lift are investigated and analyzed to determine the best production strategy for intermittent gas lift. The variables are film thickness and velocity, slug height and velocity, and gas height and velocity. The relationships between surface injection control parameters (gas injection pressure and gas injection rate) and the velocity and height of film, gas, and liquid are shown in one cycle of the gas lift intermittent process. The higher the gas injection pressure, the faster the gas injection velocity, and the thinner the film thickness in the tubing. In order to obtain clean tubing from film thickness, the gas injection pressure needs to be optimized, which will lead to maintaining compressor discharge pressure availability. Detailed observation of the dynamic performance inside the tubing production well will give the optimum oil production rate for oil wells under a gas lift intermittent production strategy for field application