File Name: neural networks and fuzzy systems a dynamical systems approach to machine intelligence .zip
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Drilling a high-pressure, high-temperature HPHT well involves many difficulties and challenges. One of the greatest difficulties is the loss of circulation. There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones. Most of these approaches are difficult to apply due to some constraints in the field.
The purpose of this work is to apply three artificial intelligence AI techniques, namely, functional networks FN , artificial neural networks ANN , and fuzzy logic FL , to identify the lost circulation zones.
Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors. High accuracy was achieved by the three AI models based on the root mean square error , confusion matrix, and correlation coefficient.
All the AI models identified the lost circulation zones in Well A with high accuracy where the is more than 0. ANN is the most accurate model with and. The demand for drilling high-pressure, high-temperature HPHT wells has become more significant in the petroleum industry. The main advantages of drilling HPHT wells are increasing oil production and improving economic success [ 2 ]. Drilling these wells involves some challenges and difficulties, mainly the appropriate tools for formation evaluation.
Drilling HPHT wells can result in many problems that delay the drilling operation and impact the cost. Loss of circulation is a common problem in drilling these wells. The partial or entire loss of the drilling mud from the wellbore to the formation is called loss of circulation or loss of return [ 4 ].
The losses will occur when a path for the flow exists, while the pressure inside the well is greater than the formation pressure [ 5 ]. Loss of circulation leads to poor hole cleaning due to the reduction of mud level in the borehole, which decreases its ability to transfer the cutting outside the wellbore [ 8 ].
The decrease in the mud level might reduce the hydrostatic pressure and cause a kick or blowout if the wellbore pressure became less than the formation pressure [ 9 ]. A range of methods has been used to overcome the circulation loss. The first method is adjusting the properties of drilling mud to reduce the equivalent circulation density ECD and consequently decreasing the quantity of the lost drilling mud [ 10 ]. The second method is pumping the lost circulation material LCM to seal and plug the losses [ 11 ].
Nevertheless, these methods are time-consuming and very expensive [ 12 ]. To minimize loss of return, it is essential to identify the lost circulation zones. Although various approaches are available, such as ECD, temperature profile, and resistivity [ 13 , 14 ], nevertheless, some of these approaches are impractical either due to the high cost or lack of technology or owing to inaccurate prediction of the thief zones.
Artificial intelligence AI allows computers to perform tasks that require human intelligence. According to Mohaghegh et al. A broader definition includes problem-solving, language perception, and conscious and unconscious processes [ 16 ]. AI is also known as a subfield of computer science involving the use of computers in tasks, which usually needs reasoning, knowledge, learning, and understanding abilities. ANN is an information-processing system, which attempts to imitate the performance features of the biological nervous system.
The network is adapted as a computer model, which can advance transformations, associations, or mappings between data [ 17 ]. The feature of ANN is that it does not require any physical phenomenon that explains the system under study [ 18 ].
Any nonlinear complex function can be approximated by ANN to make a relationship between input and output parameters. According to Ahmed et al. Weights and biases are used to handle the input parameters to find a relationship between the neurons and the source, so the performance of the network depends on the selection of those weights and biases.
Also, the performance of the estimation model relies on the choice of the number of hidden layers, training function, and number of neurons [ 21 ].
Fuzzy logic FL is a method of reasoning where the rules of deduction are estimated rather than precise. FL is valuable for handling information that is incomplete, inaccurate, or irresponsible. FL is closely similar to the theory of fuzzy groups that belongs to a set of objects with boundaries in which membership is a problem of degree [ 22 ].
The fuzzy system is typically used to characterize uncertainty, which is due to the imprecision of the data or insufficient input variables that have an essential effect on the results. A property or an item can be defined by categorizing it under one of the different noncrisp groups and also a degree of membership for every group [ 23 ]. The fuzzy set theory proposes that a truth value that is between 0 and 1should be added when working with noncrisp variables.
A membership function is used to define the relationship between a truth value and its variable. The membership functions can be represented by different functions such as sigmoid, Gaussian, trapezoidal, or straight lines [ 25 ]. When set membership had been defined again in this method, you can explain a reasoning system based on techniques for relating distributions [ 26 ]. The fuzzy inference system FIS is the procedure of creating a formulated mapping from an input to an output.
FIS is composed of five main parts: fuzzification interface, rule base, database, decision-making unit, and defuzzification interface, as shown in Figure 1. At first, the fuzzification interface transfers the input data into degrees of a match with linguistic values.
Databases are used in the rules for membership function, and the decision-making unit is utilized for the operations of inference. Finally, the defuzzification interface transfers fuzzy output to crisp results. A Sugino-type is also another kind of fuzzy if-then rules, where the premise part contains fuzzy sets only, whereas a nonfuzzy set defines the consequent part.
It is also known as an adaptive neurofuzzy inference system ANFIS that is a type of fuzzy logic and neural network [ 27 ]. It has the ability to extract the advantages of both fuzzy logic and neural network in a single method [ 28 ]. It uses the algorithm of backpropagation and the least squared to learn the data to alter the membership function that assists the fuzzy to train the data to be modeled [ 29 ]. A functional network FN is an extension of an ANN that comprises several layers of neurons linked to each other.
Every processing neuron makes an explicit calculation: a scalar usually monotone function of a weighted total of inputs. The function, combined with the neurons, is constant, and the weights are learned from data utilizing some famous algorithms like the least-square fitting [ 30 ].
An FN comprises the input layer of input data, an output layer, single or many computing neuron layers that appraise a group of input values, comes from the input layer, and provides a group of output values to the output layer. The computing neuron layers are associated with each other, which mean that the output from one unit is able to work as a portion of the input to another neuron. When the input parameters are provided, the output is found by the type of functional network [ 31 ].
Loss of return is influenced by various factors such as fluid properties, formation properties, and several known and unknown parameters. Therefore, it is arduous to predict. Therefore, many researchers applied the artificial intelligence to solve problems related to lost circulation such as Anifowose et al.
All these studies applied a single technique of AI to predict either the type of losses, the amount of losses, or the loss treatment, besides using many input parameters that are difficult to access in every well.
None of these studies predicted the zones of the losses or used the real-time mechanical surface drilling parameters in their predictions. The objective of this study is to predict the lost circulation zones using surface drilling parameters obtained by real-time drilling sensors. Figure 2 summarizes the processes of the methodology used in this study to predict the loss zones. Three onshore wells were selected, where the lost circulation records and the mechanical surface drilling parameters were used for this study.
The data were acquired on a per-foot basis from real-time sensors. The circulation loss occurred in the three wells, and the drilling was continued until reaching the end of the section without curing the losses.
Firstly, the data were collected from all operations involved in the three phases of the overall drilling process, i.
All missing values, such as values, and negative values, were removed. The second step was to include only the data in the drilling phase operation, while the data from the other phases were considered as unwanted. The data from the drilling phase operation were reorganized based on fresh footage, which requires human involvement to mark the minimum and maximum depths reached and eliminate any depth values beyond the maximum depth.
Then, any footage values less than the previous were removed and will be considered a tripping operation. Figure 3 b shows the data of WOB versus the depth from Well A after removing the random values and selecting the drilling phase operation.
The next stage was to further smoothen the data by eliminating the outliers or noise. Many filtration techniques, which have been implemented to allow data automation in the future, were applied to smooth the data. These techniques included movmean , movmedian , Gaussian , lowess , loess , rlowess , rloess , and sgolay. The best filtration technique is movmean , which ensures that most of the data are preserved without significantly altering them.
The performance of the sgolay filter was also found to be close to that of movmean ; however, when processing big data in real-time, movmean is preferred as it requires less computing power [ 45 ]. The movmean technique was also applied to filter the WOB parameter from Well A with a span of 2, 4, 6, 8, 10, and 5 to determine the optimum noise reduction while retaining the data structure.
The span of 5 is the best for data smoothing. Regarding the output, the only action taken was to prepare the data in the proper format. As the two relevant conditions for each well section are losses or no losses, the data were arranged, as shown in Table 1 , with the corresponding condition identified with 1 or 0.
The best approach to examine the influence of different parameters on the loss of circulation is by performing a statistical analysis. Data diversity was assessed through a comprehensive statistical analysis.
Statistical description contains a minimum, maximum, mean, range, mode, variation, kurtosis, skewness, and standard deviation. Table 2 shows the statistical analysis of Well A. The Well A data were randomly divided into two parts: the first part was used to train the model and the second part was used to test its ability to predict the values of the relevant parameters. The percentages of data used for training and testing were selected by trial and error.
Initial ANN runs were conducted using several different percentages of data for training and testing to select the best proportion on a trial and error basis. Figure 5 shows the results of all the trials for the selected training and testing data distributions. Several cases were evaluated to examine the impact of the input parameters on the prediction of lost circulation zones and enhance AI accuracy by removing the unnecessary parameters.
In each run, the effect of a specific parameter on predicting the lost circulation zones was evaluated, while keeping the other parameters constant. Figure 6 presents the results of all the trials defined in Table 3 for selecting the input parameters. Trials 1, 4, and 7 are giving the best results based on various combinations of independent parameters. Trial 1 requires six parameters that are one parameter more than each of Trial 4 and Trial 7.
Nevertheless, we believe that Trial 1 includes parameters that are physically important to detect the loss of circulation zones such as RPM and WOB. In contrast, Trials 4 and 7 are missing at least one of them.
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Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you. The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
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It seems that you're in Germany. We have a dedicated site for Germany. This book presents new concepts and implementations of Computational Intelligence CI systems based on neuro-fuzzy and fuzzy neural synergisms and a broad comparative analysis with the best-known existing neuro-fuzzy systems as well as with systems representing other knowledge-discovery techniques such as rough sets, decision trees, regression trees, probabilistic rule induction etc.
Once production of your article has started, you can track the status of your article via Track Your Accepted Article. Help expand a public dataset of research that support the SDGs. Artificial Intelligence AI is playing a major role in the fourth industrial revolution and we are seeing a lot of evolution in various machine learning methodologies. AI techniques are widely used by the practicing engineer to solve a whole range of hitherto intractable problems. This journal provides
Computational intelligent techniques, e. These methods have been used for solving control problems in packet switching network architectures. The introduction of active networking adds a high degree of flexibility in customizing the network infrastructure and introduces new functionality. Therefore, there is a clear need for investigating both the applicability of computational intelligence techniques in this new networking environment, as well as the provisions of active networking technology that computational intelligence techniques can exploit for improved operation. We report on the characteristics of these technologies, their synergy and on outline recent efforts in the design of a computational intelligence toolkit and its application to routing on a novel active networking environment.
Fuzzy Neural Network Github The integrate and fire model is a widely used model, typically in exploring the behavior of networks. Clarendon Press, Oxford, UK. An example of a neural network trained by tensorflow and executed using BNNS.
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