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.

An ontology based for drilling report classification

This paper presents an application of an ontology based system for automated text analysis using a sample of a drilling report to demonstrate how the methodology works. The methodology used here consists basically of organizing the knowledge related to the drilling process by elaborating the ontology of some typical problems. The whole process was carried out with the assistance of a drilling expert, and by also using software to collect the knowledge from the texts. Finally, a sample of drilling reports was used to test the system, evaluating its performance on automated text classification.

Development of intelligent systems for well drilling and petroleum production

Domains where knowledge representation is too complex to be described analytically and in a deterministic way is very common in the petroleum industry, particularly in the field of exploration and production. In these domains, applications of artificial intelligence techniques are very suitable, especially in cases where the preservation of corporate and technical knowledge is important. The Laboratory for Research on Artificial Intelligence Applied to Petroleum Engineering (LIAP) at Unicamp, has, during the last 10 years, dedicated research efforts to build intelligent systems in well drilling and petroleum production fields. In the following sections, recent advances in intelligent systems, under development in the research laboratory, are described. (C) 2001 Published by Elsevier B.V. B.V.

Comparative analysis of drilling frequencies in recent brachiopod-mollusk associations from the Southern Brazilian Shelf

Over 14,000 specimens-5,204 brachiopods, 9,137 bivalves, and 178 gastropods-acquired from 30 collecting stations (0 to 45 m depth) in the Ubatuba and Picinguaba bays, southern Brazil, were compared for drilling frequencies. Beveled (countersunk) circular-to-subcircular borings (Oichnus-like drill holes) were found in diverse bivalves but also in the rhynchonelliform brachiopod Bouchardia rosea-a small, semi-infaunal to epifaunal, free-lying species that dominates the brachiopod fauna of the southern Brazilian shelf. Drill holes in bivalve mollusks and brachiopods are comparable in their morphology, average diameter, and diameter range, indicating attacks by a single type of drilling organism. Drill holes in brachiopods were rare (0.4%) and found only at five sampling sites. Drillings in bivalves were over 10 times as frequent as in brachiopods, but the average drilling frequency was still low (5.6%) compared to typical boring frequencies of Cenozoic mollusks. Some common bivalve species, however, were drilled at frequencies up to 50 times higher than those observed for shells of B. rosea from the same samples. Due to scarcity of drilled brachiopods, it is not possible to evaluate if the driller displayed a nonrandom (stereotyped) site, size, or valve preference. Drilled brachiopods may record (1) naticid or muricid predation, (2) predation by other drillers, (3) parasitic drillings, and (4) mistaken or opportunistic attacks. Low drilling frequency in brachiopods is consistent with recent reports on ancient and modern examples. The scarcity of drilling in brachiopods, coupled with much higher drilling frequencies observed in sympatric bivalves, suggests that drilling in brachiopods may have been due to facultative or erroneous attacks. The drilling frequencies observed here for the brachiopod-bivalve assemblages are remarkably similar to those reported for Permian brachiopod-bivalves associations. This report adds to the growing evidence for an intriguing macroecological stasis: multiple meta-analytical surveys of present-day and fossil rhynchonelliform brachiopods conducted in recent years also point to persistent scarcity and low intensity of biotic interactions between brachiopods and drilling organisms throughout their evolutionary history.

EFFECT OF FENCAMFAMINE ON AVOIDANCE PERFORMANCES OF RATS

The effects of fencamfamine (1.0 and 5.0 mg/kg, ip, single dose) on an inhibitory task were studied in rats (N = 15 per group). Post-training treatment with fencamfamine (1.0 mg/kg) significantly increased avoidance latency from 23 +/- 3 to 146 +/- 28 and 170 +/- 33 s for training day 1 and day 7, respectively, indicating an enhancement of retention. However, retention was significantly reduced with a high dose of fencamfamine (5.0 mg/kg). These results demonstrate that fencamfamine caused a reproducible dose-related increase and reduction in avoidance latency.

Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between 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 an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.

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.

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.

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.

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.

Aerobic and anaerobic performances in tethered swimming

The purpose of this study was to investigate whether the critical force (CritF) and anaerobic impulse capacity (AIC) - estimated by tethered swimming - reflect the aerobic and anaerobic performance of swimmers. 12 swimmers performed incremental test in tethered swimming to determine lactate anaerobic threshold (AnTLAC), maximal oxygen uptake (̇VO2MAX) and force associated with the ̇VO2MAX (i ̇VO2MAX). The swimmers performed 4 exhaustive (tlim) exercise bouts (100, 110, 120 and 130% i ̇VO2MAX) to compute the CritF and AIC (F vs. 1/tlim model); a 30-s all-out tethered swimming bout to determine their anaerobic fitness (ANF); 100, 200, and 400-m time-trials to determine the swimming performance. CritF (57.09±11.77 N) did not differ from AnTLAC (53.96±11.52 N, (P>0.05) but was significantly lower than i ̇VO2MAX (71.02±8.36 N). In addition, CritF presented significant correlation with AnTLAC (r=0.76; P<0.05) and i ̇VO2MAX (r=0.74; P<0.05). On the other hand, AIC (286.19±54.91 N.s) and ANF (116.10±13.66 N) were significantly correlated (r=0.81, p<0.05). In addition, CritF and AIC presented significant correlations with all time-trials. In summary, this study demonstrates that CritF and AIC can be used to evaluate AnTLAC and ANF and to predict 100, 200, and 400-m free swimming. © Georg Thieme Verlag KG Stuttgart . New York.