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.

Barriers to environmental management in clusters of small businesses in Brazil and Japan: from a lack of knowledge to a decline in traditional knowledge

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem

This paper analyses the impact of choosing good initial populations for genetic algorithms regarding convergence speed and final solution quality. Test problems were taken from complex electricity distribution network expansion planning. Constructive heuristic algorithms were used to generate good initial populations, particularly those used in resolving transmission network expansion planning. The results were compared to those found by a genetic algorithm with random initial populations. The results showed that an efficiently generated initial population led to better solutions being found in less time when applied to low complexity electricity distribution networks and better quality solutions for highly complex networks when compared to a genetic algorithm using random initial populations.

Condutividade hidráulica e resistência à penetraçao do solo influenciada por diferentes sistemas de manejo

Soil management measures that increase the efficiency of organic matter cycling and maintain favorable soil structure are needed for improving soil quality. On the other hand, soil structure degradation due to inadequate soil management systems is widespread. Among the indicators of soil physical quality, saturated hydraulic conductivity and penetration resistance are thought to be sensitive to soil management system. The aim of this work was to study the influence of soil tillage system and organic fertilization on selected soil physical properties after the first year of treatment. The field work was conducted in Selviria, MS, Brazil on an Oxisol. The experimental design was randomized complete blocks with split-plots, with 12 treatments and 4 repetitions. Tillage treatments included conventional ploughing (CT) and direct drilling (DD). Fertilizer treatments were: 1) manure, 2) manure plus mineral, 3) traditional mineral 4) plant residues of Crotalaria juncea, 5) plant residues of Pennisetum americanum and 6) control plot. The plots were cropped to bean in winter and to cotton in summer, and both cultures were irrigated. After one year no significant differences between treatments in mechanical resistance and porosity were found. However, saturated hydraulic conductivity and infiltration were higher in the conventional tillage treatment at the 0.00-0.10 m depth. Moreover, an improvement in soil physical condition by organic fertilizers was shown.

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.

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.

Comparative study of algorithms for mining association rules: Traditional approach versus multi-relational approach

The multi-relational Data Mining approach has emerged as alternative to the analysis of structured data, such as relational databases. Unlike traditional algorithms, the multi-relational proposals allow mining directly multiple tables, avoiding the costly join operations. In this paper, is presented a comparative study involving the traditional Patricia Mine algorithm and its corresponding multi-relational proposed, MR-Radix in order to evaluate the performance of two approaches for mining association rules are used for relational databases. This study presents two original contributions: the proposition of an algorithm multi-relational MR-Radix, which is efficient for use in relational databases, both in terms of execution time and in relation to memory usage and the presentation of the empirical approach multirelational advantage in performance over several tables, which avoids the costly join operations from multiple tables. © 2011 IEEE.

Traditional and novel methods for occlusal caries detection: Performance on primary teeth

This study aimed to assess the performance of International Caries Detection and Assessment System (ICDAS), radiographic examination, and fluorescence-based methods for detecting occlusal caries in primary teeth. One occlusal site on each of 79 primary molars was assessed twice by two examiners using ICDAS, bitewing radiography (BW), DIAGNOdent 2095 (LF), DIAGNOdent 2190 (LFpen), and VistaProof fluorescence camera (FC). The teeth were histologically prepared and assessed for caries extent. Optimal cutoff limits were calculated for LF, LFpen, and FC. At the D 1 threshold (enamel and dentin lesions), ICDAS and FC presented higher sensitivity values (0.75 and 0.73, respectively), while BW showed higher specificity (1.00). At the D 2 threshold (inner enamel and dentin lesions), ICDAS presented higher sensitivity (0.83) and statistically significantly lower specificity (0.70). At the D 3 threshold (dentin lesions), LFpen and FC showed higher sensitivity (1.00 and 0.91, respectively), while higher specificity was presented by FC (0.95), ICDAS (0.94), BW (0.94), and LF (0.92). The area under the receiver operating characteristic (ROC) curve (Az) varied from 0.780 (BW) to 0.941 (LF). Spearman correlation coefficients with histology were 0.72 (ICDAS), 0.64 (BW), 0.71 (LF), 0.65 (LFpen), and 0.74 (FC). Inter- and intraexaminer intraclass correlation values varied from 0.772 to 0.963 and unweighted kappa values ranged from 0.462 to 0.750. In conclusion, ICDAS and FC exhibited better accuracy in detecting enamel and dentin caries lesions, whereas ICDAS, LF, LFpen, and FC were more appropriate for detecting dentin lesions on occlusal surfaces in primary teeth, with no statistically significant difference among them. All methods presented good to excellent reproducibility. © 2012 Springer-Verlag London Ltd.

How non-traditional are non-traditional exports? The experience of seven countries of the Caribbean Basin

Includes bibliography

Monitoring in precision metal drilling process using multi-sensors and neural network

This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes. © 2012 Springer-Verlag London Limited.

Influence of Agroforestry and Traditional Management of Maize (Zea mays L.) on Soil Fertility

The aim of the work was to evaluate soil nutrient concentration at 0-5, 5-10, and 10-20 cm in maize (Zea mays L.) grown in sequence with black oats (Avena strigosa Schreb.) under Leucaena diversifolia alley cropping agroforestry system (AFS) and traditional management system/sole crop (without trees-TS), following a randomized block design. The experiment was carried out at the Brazilian Association of Biodynamic Agriculture, in Botucatu, São Paulo, Brazil. The treatments were: control (C), chemical fertilizer (F), biomass of L. diversifolia alley cropping (B), and biomass of L. diversifolia alley cropping + chemical fertilizer (B+F). After 2 yr, it was observed that pH, organic matter, and nutrient content had a tendency to show higher values in the treatments biomass+fertilizer, biomass, and fertilizer application, in both systems. Higher values in pH, organic matter, phosphorus, potassium, calcium, magnesium, sum of bases, cation exchange capacity, percentage base saturation, boron, copper, and manganese tended to occur in the agroforestry system. © 2013 Copyright Taylor and Francis Group, LLC.