Chefi Ketata

Knowledge Selection for Laser Drilling in the Oil and Gas Industry

To maximize oil and gas production profitability, it is paramount to reach the whole reservoir and extract the oil and gas in it efficiently. Therefore the well should be drilled as quickly as possible to start production as soon as possible. The laser drilling literature reviews, lab experiments, and numerical models have proven so far that this novel technique performs better and faster than the conventional rotary drilling technique. In addition, this eliminates the problem of contaminated water, soil, and rocks due to the use of toxic muds while rotary drilling. As a result, the drilling and production operations costs are minimized. This paper introduces the knowledge elements for laser drilling system that will be optimized by an expert system. This system comprises oil and gas well parameters, rock properties, and laser properties and parameters. Based on these data, the corresponding rate of penetration and drilling time are evaluated

Development of expert systems for stream sampling in mineral processing plants

Abstract Stream sampling is essential for the performance assessment of a mineral processing plant. This process generates errors that are caused by stream material heterogeneity and incorrect cutter features. To control the sampling process efficiently, it is very important to evaluate and minimize the sampling errors. The objective of this paper is to introduce two expert systems for stream sampling in mineral processing plants. The first one is intended to inspect the correctness of sampling operations. It is called Sampling Correctness Inspector (SCI). The second one is destined for the evaluation of sampling errors. It is named Sampling Error Evaluator (SEE). These expert systems are validated successfully.

FUZZY EVALUATION OF STREAM SAMPLE RELIABILITY

The operation of a mineral-processing plant is investigated regularly by stream sampling. Sampling errors are generated due to material heterogeneity and cutter features. This article presents an innovative method using fuzzy logic to assess the sample reliability, which is assumed to be influenced by the cutter geometry, speed, layout, and path. Fuzzy logic permits sampling situations to be described and processed in linguistic terms such as very reliable, reliable, adequate, doubtful, and very doubtful. Each sample is assigned a sampling performance index. This is the basis for decision-making concerning sampling strategy and the weighting factors in data-reconciliation techniques.

Expert System Knowledge Management for Laser Drilling in the Oil and Gas Industry

The main drilling technique in the oil and gas industry has been rotary. Nevertheless, during the last decade, laser drilling has been investigated. Intensive research work has examined the soundness of this new drilling technique and its profitability. The laser drilling literature has confirmed that this novel technique performs better and faster than the conventional rotary drilling technique. Since the drilling time is reduced, the drilling costs are decreased, and the project profitability is increased. Furthermore, laser drilling eradicates the problem of contaminated water, soil, and rocks since water is used instead of toxic muds while drilling. This paper introduces the knowledge management process for an expert system that is the laser drilling system optimizer (LDSO). Then the development stages are described. The LDSO is an innovation since it is the first expert system developed for laser drilling in the oil and gas industry. It is a knowledge-based optimization system

STREAM MATERIAL VARIABLES AND SAMPLING ERRORS

Stream sampling is necessary to assess and control a mineral-processing plant operation properly. However, the sampling process is always marred by errors. Therefore, it is paramount to evaluate the sampling errors correctly. This article studies the influence of independent composition and mass flow rate variations on stream sampling errors, which is a novel approach. The value of each variable series at a certain time depends on a random disturbance. In this case, the various mineral-processing variables are described by time series models. This article shows that the samples collected from streams with higher autocorrelations are more representative.

Multiple-Solution Nature of Chaos Number-Oriented Equations

Nature is characterized by its chaotic behavior. The forces and processes that individually and collectively control the phenomena of the universe independently of human intervention are described by various equations. These equations need to be solved to understand the behavior of nature. Unfortunately, conventional mathematics has not been able to deal with all nature problems. Therefore, there is an urgent need for novel mathematics to solve complex problems. This paper lays the foundation for solving nature chaotic equations and shows how the universe dimensions determine the nature and number of solutions. It introduces the process to solve and identify the solutions for the simple linear equation x = 2 and quadratic equation x2 = 4 in zero, one, two, three, and more dimensions. As a result, this paper establishes an important fact for chaos equations that is all equations have multiple solutions.

INTELLIGENT CONTROL SYSTEM FOR STREAM SAMPLING

This article introduces an intelligent sampling controller to assist the actions taken by a plant operator to correct sampling conditions. This hybrid system includes a sampling error filter (SEF), a sampling performance indexer (SPI), a sampling correctness inspector (SCI), and a sampling error evaluator (SEE). First, the SEF upgrades the measured variables in a mineral-processing system by material balance. Then, the SPI employs fuzzy logic to assess the sampling performance. In addition, the expert system SCI checks the correctness of the sampling process. Finally, the sampling error is computed by the expert system SEE.

Chaos Numbers

Nature is characterized by its chaotic behavior. To understand better its phenomena, it is essential to use appropriate tools to achieve the best definition possible of its chaos variables and processes. One of the main tools is mathematics. Mathematics deals with numbers. Classical mathematics falls short since it does not take into account the dynamic and evolutionary state of numbers. This paper introduces a novel meaning for numbers. Then, various arithmetic operations are established. Such operations comprise addition, subtraction, multiplication, and division. Addition is the process of calculating the sum of two or more numbers. Subtraction is the act of deducting one number from another. Multiplication is the procedure of adding a number to itself a particular number of times. Division is the operation of calculating the number of times one number is contained in another. This paper lays the foundation for chaos arithmetic, which deals with nature perpetual evolution.

The Meaningful Infinity

Chaos is the order of nature. Nature is characterized by its chaotic variables and processes. These variables and processes are described by numbers and words. Numbers in classical mathematics are static and rigid. However, nature is dynamic and evolutionary. Therefore, it is vital to propose new definitions and meanings for the numbers in order to describe properly nature behavior and therefore know its chaos. For example, the mysterious numbers zero and infinity lack precision. Infinity is defined as being always unlimited. However, when solving mathematical equations, infinity becomes limited, which is contradictory to the previous and original concept. This paper introduces infinity as a dynamic and evolutionary number, which adds meaning and precision to it. The various chaos arithmetic operations involving infinity are defined. They include addition, subtraction, multiplication, and division. This paper represents a novel and essential contribution to establish a new branch of mathematics that is chaos arithmetic.

MATERIAL BALANCE AND STREAM SAMPLING ERRORS

This article investigates the influence of the data variation on sampling errors throughout a two-stage flotation circuit. The material balance technique is used to upgrade the measured variables. The sampling errors are evaluated before and after material balance. In one case, the covariance terms between the components in a stream and between the streams in the flotation circuit are included. In the other case, these terms are not included. This article proves that by including the covariance terms in the calculation, the variances of the sampling errors are reduced and, therefore, the reliability of the material balance is improved.