Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Identification of lift-off mechanism failure for salt drill-in drilling fluid containing polymer filter cake through adsorption/desorption studies

Drilling fluid`s contact with the productive zone of horizontal or complex wells can reduce well productivity by fluid invasion in the borehole wall. Salted drilling drill-in fluid containing polymers has often been applied in horizontal or complex petroleum wells in the poorly consolidated sandstone reservoirs of the Campos basin, Rio de Janeiro, Brazil. This fluid usually consists of natural polymers such as starch and xanthan gum, which are deposited as a filter cake on the wellbore wall during the drilling. Therefore, the identification of a lift-off mechanism failure, which can be detachment or blistering and pinholing, will enable formulation improvements. increasing the chances of success during filter cake removal in open hole operations. Likewise, knowledge of drill-in drilling fluid adsorption/desorption onto sand can help understand the filter cake-rock adhesion mechanism and consequently filter cake lift-off mechanism failures. The present study aimed to identify the lift-off failure mechanism for this type of fluid filter cake studying adsorption/desorption onto SiO(2) using solutions of natural polymers, lubricants, besides the fluid itself. Ellipsometry was employed to measure this process. The adsorption/desorption studies showed that the adsorbed layer of drilling fluid onto the walls of the rock pores is made up of clusters of polymers, linked by hydrogen bonds, which results in a force of lower cohesion compared to the electrostatic interaction between silica and polymers. Consequently, it was found that the most probable filter cake failure mechanism is rupture (blistering and pinholing), which results in the formation of ducts within the filter cake. (C) 2009 Elsevier B.V. All rights reserved.

An Intelligent System for Petroleum Well Drilling Cutting Analysis

Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.

Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques

Petroleum well drilling monitoring has become an important tool for detecting and preventing problems during the well drilling process. In this paper, we propose to assist the drilling process by analyzing the cutting images at the vibrating shake shaker, in which different concentrations of cuttings can indicate possible problems, such as the collapse of the well borehole walls. In such a way, we present here an innovative computer vision system composed by a real time cutting volume estimator addressed by support vector regression. As far we know, we are the first to propose the petroleum well drilling monitoring by cutting image analysis. We also applied a collection of supervised classifiers for cutting volume classification. (C) 2010 Elsevier Ltd. All rights reserved.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

An intelligent system to detect drilling problems through drilled cuttings return analysis

Cuttings return analysis is an important tool to detect and prevent problems during the petroleum well drilling process. Several measurements and tools have been developed for drilling problems detection, including mud logging, PWD and downhole torque information. Cuttings flow meters were developed in the past to provide information regarding cuttings return at the shale shakers. Their use, however, significantly impact the operation including rig space issues, interferences in geological analysis besides, additional personel required. This article proposes a non intrusive system to analyze the cuttings concentration at the shale shakers, which can indicate problems during drilling process, such as landslide, the collapse of the well borehole walls. Cuttings images are acquired by a high definition camera installed above the shakers and sent to a computer coupled with a data analysis system which aims the quantification and closure of a cuttings material balance in the well surface system domain. No additional people at the rigsite are required to operate the system. Modern Artificial intelligence techniques are used for pattern recognition and data analysis. Techniques include the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC). Field test results conducted on offshore floating vessels are presented. Results show the robustness of the proposed system, which can be also integrated with other data to improve the efficiency of drilling problems detection. Copyright 2010, IADC/SPE Drilling Conference and Exhibition.

A multi-agent architecture for intelligent data analysis

The present study introduces a multi-agent architecture designed for doing automation process of data integration and intelligent data analysis. Different from other approaches the multi-agent architecture was designed using a multi-agent based methodology. Tropos, an agent based methodology was used for design. Based on the proposed architecture, we describe a Web based application where the agents are responsible to analyse petroleum well drilling data to identify possible abnormalities occurrence. The intelligent data analysis methods used was the Neural Network.

Análise e modelagem do escoamento transitório de fluidos de perfuração

Drilling fluids present a thixotropic behavior and they usually gel when at rest. The sol-gel transition is fundamental to prevent the deposit of rock fragments, generated by drilling the well, over the drill bit during eventual stops. Under those conditions, high pressures are then required in order to break-up the gel when circulation is resumed. Moreover, very high pressures can damage the rock formation at the bottom of the well. Thus, a better understanding of thixotropy and the behavior of thixotropic materials becomes increasingly important for process control. The mechanisms that control thixotropy are not yet well defined and modeling is still a challenge. The objective of this work is to develop a mathematical model to study the pressure transmission in drilling fluids. This work presents a review of thixotropy and of different mathematical models found in the literature that are used to predict such characteristic. It also shows a review of transient flows of compressible fluids. The problem is modeled as the flow between the drillpipe and the annular region (space between the wall and the external part of the drillpipe). The equations that describe the problem (mass conservation, momentum balance, constitutive and state) are then discretized and numerically solved by using a computational algorithm in Fortran. The model is validated with experimental and numerical data obtained from the literature. Comparisons between experimental data obtained from Petrobras and calculated by three viscoplastic and one pseudoplastic models are conducted. The viscoplastic fluids, due to the yield stress, do not fully transmit the pressure to the outlet of the annular space. Sensibility analyses are then conducted in order to evaluate the thixotropic effect in pressure transmission.

Estudo da incorporação de cascalho proveniente da perfuração de poços de petróleo em formulações para tijolos de solo-cimento

The present work is to study the characteristics and technological properties of soil-cement bricks made from binary and ternary mixtures of Portland cement, sand, water, with or without addition of gravel from the drilling of oil wells, which could be used by industry, aiming to improve its performance and reduce cost by using the residue and, consequently, increasing its useful life. The soil-cement bricks are one of the alternatives to masonry construction. These elements, after a short curing period, provide compressive strength similar to that of solid bricks and ceramic blocks, and the higher the resistance the higher the amount of cement used. We used the soil from the city of São José do Mipibu / RN, the banks of the River Baldun, cement CPIIZ-32 and residue of drill cuttings from oil wells drilling onshore wells in the town of Mossley, RN, provided Petrobras. To determine the optimum mix, we studied the inclusion of different residues (100%, 80%, 70%, 60% and 50%) where 15 bodies were made of the test piece. The assessment was made of bricks made from simple compression tests, mass loss by immersion and water absorption. The experimental results proved the efficiency and high utilization of the waste from the drilling of oil wells, making the brick-cement-soil residue with a higher strength and lower water absorption. The best result in terms of mechanical strength and water absorption for the ternary mixture was 10% soil, 14% cement and 80% residue. In terms of binary mixtures, we obtained the best result for the mix-cement residue, which was 14% cement incorporated in the residue

Síntese enzimática de ésteres de açúcar: surfactantes e polímeros como novos materiais ambientalmente seguros

Sugar esters are substances which possess surfactant, antifungical and bactericidal actions and can be obtained through two renewable sources of raw materials: sugars and vegetable oils. Their excellent biodegradability, allied to lhe fact that they are non toxic, insipid, inodorous, biocompatible, no-ionic, digestible and because they can resist to adverse conditions of temperature, pH and salinity, explain lhe crescent use of these substances in several sections of lhe industry. The objective of this thesis was to synthesize and characterize surfactants and polymers containing sugar branched in their structures, through enzymatic transesterification of vinyl esters and sugars, using alkaline protease from Bacillus subtilis as catalyst, in organic medium (DMF).Three types of sugars were used: L-arabinose, D-glucose and sucrose and two types of vinyl esters: vinyl laurate and vinyl adipate. Aiming to reach high conversions from substrates to products for a possible future large scale industrial production, a serie of variables was optimized, through Design of Experiments (DOE), using Response Surface Methodology (RSM).The investigated variables were: (1) enzyme concentration; (2) molar reason of substrates; (3) water/solvent rale; (4) temperature and (5) time. We obtained six distinct sugar esters: 5-0-lauroyl L-arabinose, 6-0-lauroyl D-glucose, 1'-O-lauroyl sucrose, 5-0-vinyladipoyl L-arabinose, 6-0-vinyladipoyl D-glucose and 1 '-O-vinyladipoyl sucrose, being lhe last three polymerizable. The progress of lhe reaction was monitored by HPLC analysis, through lhe decrease of sugar concentration in comparison to lhe blank. Qualitative analysis by TLC confirmed lhe formation of lhe products. In lhe purification step, two methodologies were adopted: (1) chromatographic column and (2) extraction with hot acetone. The acylation position and lhe chemical structure were determined by 13C-RMN. The polymerization of lhe three vinyl sugar esters was possible, through chemical catalysis, using H2O2 and K2S2O8 as initiators, at 60°C, for 24 hours. IR spectra of lhe monomers and respective polymers were compared revealing lhe disappearance of lhe vinyl group in lhe polymer spectra. The molar weights of lhe polymers were determined by GPC and presented lhe following results: poly (5-0-vinyladipoyl L-arabinose): Mw = 7.2 X 104; PD = 2.48; poly (6-0-vinyladipoyl D-glucose): Mw = 2.7 X 103; PD = 1.75 and poly (1'-O-vinyladipoyl sucrose): Mw = 4.2 X 104; PD = 6.57. The six sugar esters were submitted to superficial tension tests for determination of the critical micelle concentrations (CMC), which varied from 122 to 167 ppm. Finally, a study of applicability of these sugar esters, as lubricants for completion fluids of petroleum wells was' accomplished through comparative analysis of lhe efficiency of these sugar esters, in relation to three commercial lubricants. The products synthesized in this thesis presented equivalent or superior action to lhe tested commercial products