TRATAMIENTO DIGITAL DE IMÁGENES E INTELIGENCIA ARTIFICIAL APLICADOS A LA PERFORACIÓN DE POZOS PETROLEROS

RESUMENLos avances en el procesamiento de datos aplicados en el área de perforación de pozos han sido grandes debido al auge de las tecnologías y a la inversión en investigación que realiza esta industria. Actualmente se realizan mediciones y monitoreos y se toman decisiones basadas en los resultados obtenidos de procesar e interpretar los datos tomados por diferentes herramientas dentro del pozo. Aunque diversas técnicas de procesamiento de imágenes han sido aplicadas en la industria petrolera, no están documentadas en la literatura la estimación de la profundidad de derrumbes o cavings en pozos petroleros. Este artículo describe la forma como se han aplicado diferentes técnicas computacionales, principalmente aquellas que involucran el reconocimiento de patrones a través del tratamiento digital de imágenes y la inteligencia artificial en la industria petrolera, especialmente en los procesos de exploración y perforación y, finalmente plantea una propuesta de trabajo de investigación que puede contribuir a mitigar riesgos asociados a estabilidad de pozo, facilitando la toma de decisiones en tiempo real y permitiendo realizar acciones apropiadas de prevención o remediación cuando estas son requeridas, aplicando el procesamiento de imágenes y el uso de técnicas computacionales como el reconocimiento de patrones en la estimación de la profundidad de derrumbes en pozos petroleros.Palabras clave: Tratamiento digital de imágenes, Inteligencia artificial, reconocimiento de patrones, Pozos petroleros, Rocas, Derrumbes, Recortes de roca.ABSTRACTAdvances in data processing applied in the drilling area have been high due to the rise of technology and investment in research conducted by the industry. Currently, measurements and monitoring are performed and decisions are made based on the results of processing and interpreting the data collected by different tools into the well. Although various imaging techniques have been applied in the oil industry are not documented in the literature to estimate the depth of landslides or cavings in oil wells. This article describes the way we have applied different computational techniques, especially those that involve pattern recognition through digital image processing and artificial intelligence in the oil industry, especially in exploration and drilling processes, and finally presents a proposal research work can help mitigate risks associated with wellbore stability, facilitating decision making in real time and allowing appropriate preventive actions or remediation when they are required, applying image processing and the use of computational techniques such as pattern recognition in estimating the depth of collapse in oil wells.Keywords: Digital Image Processing, Artificial Intelligence, Pattern Recognition, Oil Wells, Rocks, Caving, and Cuttings

Modelling oil and gas flow rate through chokes: A critical review of extant models

Oil and gas metering is primarily used as the basis for evaluating the economic viability of oil wells. Owing to the economic implications of oil and gas metering, the subject of oil and gas flow rate measurement has witnessed a sustained interest by the oil and gas community and the academia. To the best of the authors’ knowledge, despite the growing number of published articles on this subject, there is yet no comprehensive critical review on it. The objective of this paper is to provide a broad overview of models and modelling techniques applied to the estimation of oil and gas flow rate through chokes while also critically evaluating them. For the sake of simplicity and ease of reference, the outcomes of the review are presented in tables in an integrated and concise manner. The articles for this review were extracted from many subject areas. For the theoretical pieces related to oil and gas flow rate in general, the authors relied heavily upon several key drilling fluid texts. For operational and field studies, the authors relied on conference proceedings from the society of petroleum engineers. These sources were supplemented with articles in peer reviewed journals in order to contextualize the subject in terms of current practices. This review is interspersed with critiques of the models while the areas requiring improvement were also outlined. Findings from the bibliometric analysis indicate that there is no universal model for all flow situations despite the huge efforts in this direction. Furthermore, a broad survey of literature on recent flow models reveals that researchers are gravitating towards the field of artificial intelligence due to the tremendous promises it offers. This review constitutes the first critical compilation on a broad range of models applied to predicting oil and gas flow rates through chokes

Emerging Trends in Mechatronics

Mechatronics is a multidisciplinary branch of engineering combining mechanical, electrical and electronics, control and automation, and computer engineering fields. The main research task of mechatronics is design, control, and optimization of advanced devices, products, and hybrid systems utilizing the concepts found in all these fields. The purpose of this special issue is to help better understand how mechatronics will impact on the practice and research of developing advanced techniques to model, control, and optimize complex systems. The special issue presents recent advances in mechatronics and related technologies. The selected topics give an overview of the state of the art and present new research results and prospects for the future development of the interdisciplinary field of mechatronic systems

Plantwide simulation and monitoring of offshore oil and gas production facility

Monitoring is one of the major concerns in offshore oil and gas production platform since the access to the offshore facilities is difficult. Also, it is quite challenging to extract oil and gas safely in such a harsh environment, and any abnormalities may lead to a catastrophic event. The process data, including all possible faulty scenarios, is required to build an appropriate monitoring system. Since the plant wide process data is not available in the literature, a dynamic model and simulation of an offshore oil and gas production platform is developed by using Aspen HYSYS. Modeling and simulations are handy tools for designing and predicting the accurate behavior of a production plant. The model was built based on the gas processing plant at the North Sea platform reported in Voldsund et al. (2013). Several common faults from different fault categories were simulated in the dynamic system, and their impacts on the overall hydrocarbon production were analyzed. The simulated data are then used to build a monitoring system for each of the faulty states. A new monitoring method has been proposed by combining Principal Component Analysis (PCA) and Dynamic PCA (DPCA) with Artificial Neural Network (ANN). The application of ANN to process systems is quite difficult as it involves a very large number of input neurons to model the system. Training of such large scale network is time-consuming and provides poor accuracy with a high error rate. In PCA-ANN and DPCA-ANN monitoring system, PCA and DPCA are used to reduce the dimension of the training data set and extract the main features of measured variables. Subsequently ANN uses this lower-dimensional score vectors to build a training model and classify the abnormalities. It is found that the proposed approach reduces the time to train ANN and successfully diagnose, detects and classifies the faults with a high accuracy rate

Supervised data-driven approach to early kick detection during drilling operation

The margin between pore pressure and fracture gradient in new offshore discoveries continues to get narrower. This poses greater risks and higher cost of ensuring safety of lives, facilities, and the environment. The 2010 Macondo blowout has fueled increased interests in monitoring downhole parameter for early kick detection. Early detection of kick is important part of the process safety. It provides opportunity to activate safety measures. However, after an extensive literature search, certain gaps were identified in early kick detection research. This ranged from limited availability of downhole drilling data from oil fields with downhole pressure and flow measurements for research purposes to limited modelling efforts that applies machine learning to downhole measurements in the area of early kick detection. Leveraging machine learning is crucial because of the tremendous advancements in artificial intelligence and information technology. This research provides a simple design approach to build machine learning kick detection models. In the absence of field data, we collect data from existing and new experiments that records downhole measurements. A simple model is rewarding when data processing is done downhole. The hardware used is typically battery powered. Simpler and fewer software operations will lead to less power consumption, smaller memory and simpler cooling requirements. This will lead to an increase battery run time, miniaturized designs/reduced bulk size, reduced maintenance frequency for such hardware, improved response time and lower costs. In this thesis, we investigate the simplest supervised neural network-based machine learning kick detection system to ensure high reliability using experimental data. Building upon previous kick experiments conducted using a Small Drilling Simulator (SDS), we present a detailed design of a new kick experiment setup that uses a Large Drilling Simulator (LDS) and synthetic rock samples. We also provide a detailed design of synthetic rock sample with geometrical capability to trap high-pressure formation fluid within. The experiment setup produces new set of data from downhole parameter monitoring that will be used in testing the machine learning model. Parameters such as mud flow-out rate, conductivity, density, and downhole pressure from two previous drilling experiment that monitored downhole parameters are combined to build a data-driven model for early kick detection. This model combines an Artificial Neural Network (ANN) with a binary classifier at its output. Several input combinations are trained and tested. The model can be scaled to capture other types of drilling problems such as lost circulation and also applied in the LDS system. The model was tested and evaluated with data from the SDS system, SDS system with faulty conductivity data and different experimental drilling system. Abnormal pressure and flow regimes in the wellbore provide early warnings and are shown to be more significant parameters than others; however, solely relying on them can increase susceptibility to false alarm

A unified metaheuristic and system-theoretic framework for petroleum reservoir management

With phenomenal rise in world population as well as robust economic growth in China, India and other emerging economies; the global demand for energy continues to grow in monumental proportions. Owing to its wide end-use capabilities, petroleum is without doubt, the world’s number one energy resource. The present demand for oil and credible future forecasts – which point to the fact that the demand is expected to increase in the coming decades – make it imperative that the E&P industry must device means to improve the present low recovery factor of hydrocarbon reservoirs. Efficiently tailored model-based optimization, estimation and control techniques within the ambit of a closed-loop reservoir management framework can play a significant role in achieving this objective. In this thesis, some fundamental reservoir engineering problems such as field development planning, production scheduling and control are formulated into different optimization problems. In this regard, field development optimization identifies the well placements that best maximizes hydrocarbon recovery, while production optimization identifies reservoir well-settings that maximizes total oil recovery or asset value, and finally, the implementation of a predictive controller algorithm which computes corrected well controls that minimizes the difference between actual outputs and simulated (or optimal) reference trajectory. We employ either deterministic or metaheuristic optimization algorithms, such that the choice of algorithm is purely based on the peculiarity of the underlying optimization problem. Altogether, we present a unified metaheuristic and system-theoretic framework for petroleum reservoir management. The proposed framework is essentially a closed-loop reservoir management approach with four key elements, namely: a new metaheuristic technique for field development optimization, a gradient-based adjoint formulation for well rates control, an effective predictive control strategy for tracking the gradient-based optimal production trajectory and an efficient model-updating (or history matching) – where well production data are used to systematically recalibrate reservoir model parameters in order to minimize the mismatch between actual and simulated measurements. Central to all of these problems is the use of white-box reservoir models which are employed in the well placement optimization and production settings optimization. However, a simple data-driven black-box model which results from the linearization of an identified nonlinear model is employed in the predictive controller algorithm. The benefits and efficiency of the approach in our work is demonstrated through the maximization of the NPV of waterflooded reservoir models that are subject to production and geological uncertainty. Our procedure provides an improvement in the NPV, and importantly, the predictive control algorithm ensures that this improved NPV are attainable as nearly as possible in practice

DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS

Determination of pressure drop in pipeline system is difficult. Conventional methods (empirical correlations and mechanistic methods) were not successful in providing accurate estimate. Artificial Neural Networks and polynomial Group Method of Data Handling techniques had received wide recognition in terms of discovering hidden and highly nonlinear relationships between input and output patterns. The potential of both Artificial Neural Networks (ANN) and Abductory Induction Mechanism (AIM) techniques has been revealed in this study by generating generic models for pressure drop estimation in pipeline systems that carry multiphase fluids (oil, gas, and water) and with wide range of angles of inclination. No past study was found that utilizes both techniques in an attempt to solve this problem. A total number of 335 data sets collected from different Middle Eastern fields have been used in developing the models. The data covered a wide range of variables at different values such as oil rate (2200 to 25000 bbl/d), water rate (up to 8424 bbl/d), angles of inclination (-52 to 208 degrees), length of the pipe (500 to 26700 ft) and gas rate (1078 to 19658 MSCFD). For the ANN model, a ratio of 2: 1: 1 between training, validation, and testing sets yielded the best training/testing performance. The ANN model has been developed using resilient back-propagation learning algorithm. The purpose for generating another model using the polynomial Group Method of Data Handling technique was to reduce the problem of dimensionality that affects the accuracy of ANN modeling. It was found that (by the Group Method of Data Handling algorithm), length of the pipe, wellhead pressure, and angle of inclination have a pronounced effect on the pressure drop estimation under these conditions. The best available empirical correlations and mechanistic models adopted by the industry had been tested against the data and the developed models. Graphical and statistical tools had been utilized for comparing the performance of the new models and other empirical correlations and mechanistic models. Thorough verifications have indicated that the developed Artificial Neural Networks model outperforms all tested empirical correlations and mechanistic models as well as the polynomial Group Method of Data Handling model in terms of highest correlation coefficient, lowest average absolute percent error, lowest standard deviation, lowest maximum error, and lowest root mean square error. The study offers reliable and quick means for pressure drop estimation in pipelines carrying multiphase fluids with wide range of angles of inclination using Artificial Neural Networks and Group Method of Data Handling techniques. Graphical User Interface (GUI) has been generated to help apply the ANN model results while an applicable equation can be used for Group Method of Data Handling model. While the conventional methods were not successful in providing accurate estimate of this property, the second approach (Group Method of Data Handling technique) was able to provide a reliable estimate with only three-input parameters involved. The modeling accuracy was not greatly harmed using this technique