Applications of Ultrasonic Techniques in Oil and Gas Pipeline Industries: A Review

The diversity of ultrasound techniques used in oil and gas pipeline plants provides us with a wealth of information on how to exploit this technology when combined with other techniques, in order to improve the quality of analysis. The fundamental theory of ultrasonic nondestructive evaluation (NDE) technology is offered, along with practical limitations as related to two factors (wave types and transducers). The focus is limited to the two main techniques used in pipe plants: First, straight beam evaluation and second, angle beam evaluation. The depth of defect (DD) is calculated using straight beam ultrasonic in six different materials according to their relative longitudinal wave (LW) velocities. The materials and respective velocities of LW are: rolled aluminum (6420 m/s), mild steel (5960 m/s), stainless steel-347 (5790 m/s), rolled copper (5010 m/s), annealed copper (4760 m/s), and brass (4700 m/s). In each material eight defects are modeled;the first represents l00% of the material thickness (D), 50.8 mm. The other seven cases represent the DD, as 87.5% of the material thickness, 75%, 62.5%, 50%, 37.5%, 25%, and 12.5%, respectively. Using angle beam evaluation, several parameters are calculated for six different reflection angles (βR) (45°, 50°, 55°, 60°, 65° and 70°). The surface distance (SD), ½skip distance (SKD), full SKD, and 1½SKD,½sound path (SP) length, full SP, and 1½SP are calculated for each βR. The relationship of SKD and SP to the βR is graphed. A chief limitation is noted that ultrasound testing is heavily dependent on the expertise of the operator, and because the reading of the outcome is subjective, precision may be hard to achieve. This review also clarifies and discusses the options used in solving the industrial engineering problem, with a comprehensive historical summary of the information available in the literature. Merging various NDE inspection techniques into the testing of objects is discussed. Eventually, it is hoped to find a suitable technique combined with ultrasonic inspection to deliver highly effective remote testing.

Enhancing Production Efficiency of Oil and Natural Gas Pipes Using Microwave Technology

The research reported in this paper aims at developing means of Non Destructive testing (NDT) to increase the line efficiency of pipe production in oil and natural gas pipe manufacturing plants using the Standard Allowed Minutes (SAM) method. Existing line production stations encounter difficulties in maintaining the recommended testing speed of smaller diameter pipe, due to limitations in the Visual Inspection (VI) station. We propose to implement one additional technique which will prevent the decline of line efficiency in a pipe production factory. The range of diameters identified as a problem in this research is from 254 mm to 762 mm. Microwave techniques are expected to improve the line efficiency by increasing the production of the plant. This happens as a consequence of maintaining the production rates of the identified pipe diameters, so that they equal the production output of the larger pipe diameters. We analyze the velocity traveled by the pipe through Radiographic Testing (RT) according to the VI output (production). The RT velocity is decreased for the diameters identified above, in order to maintain quality control and cover the shortcoming of the VI. The number of pipes produced is computed during shift hours of the factory and pipe lengths of the forming department are determined. We compare the output (production) of a series of NDT line stations with and without the microwave technique for the first of the three pipe cases considered in this study, classified as perfect pipe (PP), repair pipe (RP) and scrap pipe (SP). The velocity of RT stations analyzed in the paper ranges from 50 mm/s for larger diameter pipe, and decline to 16.667 mm/s for the identified diameters. The analytical calculations of line output (production) and line efficiency demonstrate the solution of this velocity problem after the microwave technique is introduced. It demonstrates that an economical and precise methodology to extend the production capability of the pipe plant has been determined.

Decision Support through Intelligent Agent Based Simulation and Multiple Goal Based Evolutionary Optimization

Agent based simulation has successfully been applied to model complex organizational behavior and to improve or optimize aspects of organizational performance. Agents, with intelligence supported through the application of a genetic algorithm are proposed as a means of optimizing the performance of the system being modeled. Local decisions made by agents and other system variables are placed in the genetic encoding. This allows local agents to positively impact high level system performance. A simple, but non trivial, peg game is utilized to introduce the concept. A multiple objective bin packing problem is then solved to demonstrate the potential of the approach in meeting a number of high level goals. The methodology allows not only for a systems level optimization, but also provides data which can be analyzed to determine what constitutes effective agent behavior.

A Rule Based Evolutionary Optimization Approach for the Traveling Salesman Problem

The traveling salesman problem has long been regarded as a challenging application for existing optimization methods as well as a benchmark application for the development of new optimization methods. As with many existing algorithms, a traditional genetic algorithm will have limited success with this problem class, particularly as the problem size increases. A rule based genetic algorithm is proposed and demonstrated on sets of traveling salesman problems of increasing size. The solution character as well as the solution efficiency is compared against a simulated annealing technique as well as a standard genetic algorithm. The rule based genetic algorithm is shown to provide superior performance for all problem sizes considered. Furthermore, a post optimal analysis provides insight into which rules were successfully applied during the solution process which allows for rule modification to further enhance performance.

Topological Design via a Rule Based Genetic Optimization Algorithm

A topological structural design approach is presented which is based upon the implementation of a two phase evolutionary optimization algorithm in conjunction with a finite element analysis code. The first phase utilizes a conventional genetic approach which performs a global search for the optimal design topology. Dual level material properties are specified within the genetic encoding and are applied to each individual element in the design mesh to represent either design material or a void. The second phase introduces a rule based refinement which allows for user design intent to accelerate the solution process and eliminate obvious design discrepancies resulting from the phase one search. A series of plate design problems are presented where the objective is to minimize the overall volume of the structure under predefined loading and constraint conditions. The constraints include both stress and deflection considerations where stress is calculated through the use of a commercial finite element package. The initial plate example incorporates a coarse mesh, but a gradual decrease in element size was employed for the remaining cases examined. Replacement of the phase one search with a set of randomly generated designs is demonstrated in order to form a greatly reduced design space which drastically increases the efficiency of the solution process. Comparison results are drawn between the conventional genetic algorithm and the two phase procedure.

Maze Navigation via Genetic Optimization

One of the most interesting applications of genetic algorithms falls into the area of decision support. Decision support problems involve a series of decisions, each of which is influenced by all decisions made prior to that point. This class of problems occurs often in enterprise management, particularly in the area of scheduling or resource allocation. In order to demonstrate the formulation of this class of problems, a series of maze problems will be presented. The complexity of the mazes is intensified as each new maze is introduced. Two solving scenarios are introduced and comparison results are provided. The first scenario incorporated the traditional genetic algorithm procedure for the intended purpose of acquiring a solution based upon a purely evolutionary approach. The second scenario utilized the genetic algorithm in conjunction with embedded domain specific knowledge in the form of decision rules. The implementation of domain specific knowledge is intended to enhance solution convergence time and improve the overall quality of offspring produced which significantly increases the probability of acquiring a more accurate and consistent solution. Results are provided below for all mazes considered. These results include the traditional genetic algorithm final result and the genetic algorithm optimization approach with embedded rules result. Both results were incorporated for comparison purposes. Overall, the incorporation of domain specific knowledge outperformed the traditional genetic algorithm in both performance and computation time. Specifically, the traditional genetic algorithm failed to adequately find an acceptable solution for each example presented and prematurely converged on average within 54% of their specified generations. Additionally, the most complex maze generated an optimal path directional sequence (i.e. N, S, E, W) via a traditional genetic algorithm which possessed only 50% of the required allowable path sequences for maze completion. The incorporation of embedded rules enabled the genetic algorithm to locate the optimum path for all examples considered within 5% of the traditional genetic algorithm computation time.

An Interactive Evolutionary Environment for Creative Design

A creative and/or innovative computer aided design environment is developed around the concept of an evolutionary optimization algorithm. Designs are generated within a set of prescribed design frameworks using a problem specific encoding and modified through operations including recombination, crossover and mutation. Evaluation of all candidate designs is performed by a user through a graphical user interface. A set of problems involving the creation of graphic images is presented. The examples include the generation of a set of two dimensional polygonal shapes, fractal images, path generation from a multi-degree freedom mechanical planar linkage and a mathematically prescribed pattern generation from a graphic design application utilized in quilting. Post design evaluation of the user input to the process provides insight into the individuals design strategy as well as determination of common user attributes in the creative design process.

Structural Design via Genetic Optimization

Structural designs (i.e. truss structures) are derived by the use of a three phase genetic optimization approach, where the minimization of volume is the objective of each truss structure considered. A genetic algorithm is employed which controls the three phase optimization technique. The first phase utilizes the conventional functionality of the genetic algorithm from an evolutionary perspective, however designer interaction by the use of constant rules is provided to ensure an effective evolutionary search outcome. The second phase enhances the best design constructed from phase one by the use of domain specific knowledge in the form of design rules. Phase three improves the final design assembled within phase two by the reduction of truss element areas. This refinement process ensures that the design constraints provided are active, indicating an optimal search solution. All phases operate from a global perspective;however the phase two optimization methodology operates from a more radical approach which encompasses the concept of designing from a “blank sheet of paper” point of view. Results are provided upon the conclusion of each truss example considered which includes the outcomes of each phase for comparison purposes.