Permeability and porosity have a significant impact upon field operations and reservoir management. Combined measurement of these properties from cores and well tests can provide the best results, but the cost may be prohibitive for routine use. The common correlation used to predict permeability is obtained from the graph of the logarithm of core permeability versus core porosity. However, sometimes the correlation coefficients are not good, often less than 0.6. This correlation assumes that the permeability is only a function of porosity. Investigations have shown that permeability is not only a function of porosity, but also of true resistivity, irreducible water saturation, hydrocarbon density and rock type. Determination, prediction, or estimation of permeability using those variables without actual core measurements has been a fundamental problem for petroleum engineers and geoscientists. Neural networks have shown great potential for generating accurate analyses and results that otherwise seem not to be useful or relevant in the analysis of large amount of data. In this work, a neural network model has been developed using core data and well log analysis to predict permeability and porosity of zone "C" of the Cantagallo field in Colombia. The algorithm used in this case was back propagation. The input variables were gamma ray, true resistivity, spontaneous potential, and neutron porosity from logs. Core permeability and porosity data were obtained from the Yarigui-l3 and Yarigui-l2 wells. These data were necessary for training and testing the neural network. The correlation coefficients obtained from conventional statistical analysis for permeability and porosity for this field were 0.598 and 0.396, respectively. The correlation coefficients for the permeability and porosity of the neural network models were 0.996 and 0.979, respectively. New oil zones were selected to be perforated in wells Yarigui-69 and Yarigui-l7 because of the attractive permeability obtained from the neural network analysis. The production was increased in each well by at least 120 bopd. Introduction The importance of permeability is reflected by the number of approaches that have been developed for its evaluation. They can be categorized into three major techniques:laboratory testing of core samples,well test analysis, andwell log analysis. An early correlation of permeability with porosity and intergranular area was proposed by Kozeny in 1927 and then modified by Carman. Their equation is as follows: (1) Berg derived a correlation of permeability with porosity and grain diameter on the basis of a systematic packing of spherical particles:

During the last few years, research has been done on generalized material balance equations for conventional oil and gas reservoirs in order to improve the reservoir performance analysis. However, those equations are inappropriate for coal seam gas (CSG) reservoirs. To address this limitation, a generalized material balance equation (GMBE) for CSG reservoirs was developed. This work is based on a mathematical development and the straight-line method, published previously and widespread used for conventional reservoirs. Three validation examples of the proposed equation were designed. They show the new equation has the following advantages:it is applicable to CSG reservoirs in saturated, equilibrium, and undersaturated conditions,it is applicable to any type of coalbed without restriction on especial diffusion constant values,existent equations are particular cases of the generalized equation evaluated under certain restrictions, andits reorganization is analogous to the popular straight- line method for conventional reservoirs. P. 621

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Different knowledge areas on the petroleum industry require to solve problems related with data transformation processes needed to generate information and knowledge. This paper describes data analysis steps using Artificial Intelligent techniques which include the problem exploration, space analysis, surveying data source, data preparation and building the appropriate data mining model. Predictive and inferential models are illustrated by applications implemented using Unsupervised Artificial Neural Networks and a Fuzzy Rule Diagnosis System. (1) The first application is able to identify well zones potentially producing hydrocarbons in Colombian PETROLEA field. This reservoir is mainly a fractured and calcareous formation. The knowledge predictive model uses Fuzzy Learning Vector Quantization (FLVQ) built from historical production tests data and spontaneous potential, short and long resistivity well logs used for training, testing and validating the model. The final Bravais & Pearson correlation factor obtained is 0.95. (2) Finally a Fuzzy Rule Diagnosis System is applied to enhanced oil recovery screening comparing technical information from reservoir, well and oil properties. The model uses heuristic and lab results for knowledge base implementation adapted to Colombian oil fields. Introduction Conventional methods of data interpretation in reservoir engineering use algorithmic process, stochastic and empirical models that are adjusted at the phenomena conditions. Its goal is to verify the hypothesis previously adopted with validated patterns. The source of data can be experimental or lab measurements and recording or direct observations. The data analysis must be extensive to the processes and interactions among them. These data correlate with attribute values to define an object in a time domain. Furthermore, it must be considered the complexity and uncertainty generated when the data are translated from a natural language to a machine language. New techniques in Artificial Intelligence (IA), like neural networks, fuzzy logics, Knowledge based systems, experts systems and genetic algorithms, and others are very useful in data analysis1,2,3,4. Those techniques are applied to prediction process, diagnostic and no lineal complex transforms with a high degree of uncertainty. Claude E. Shannon5 pioneer of the Information Theory defined it as a tool for improved uncertainty handling in the real world. These problems of patterns predictions, correlations, transformations and decisions taking are supported by the availability, quality and transformation of the data source of the information and knowledge. The data analysis methods search direct interaction among the expert, his knowledge and the available information to generate knowledge models. It initiate with the problem exploration and definition, the identity of the outside variables and the space, the characterization of de solutions methods and the applications of data mining that include the data preparation, inspection and modeling. The final solution depends directly of the expert knowledge, using two generic strategies, the first is called "down-top strategy" and uses data recorded through the time in static or dynamic way. This strategy requires data mining techniques to generate knowledge as diagnosis rules, clustering models, and non-linear predictive models. A Fuzzy Learning Vector Quantization (FLVQ) model is designed to identify well zones potentially producing hydrocarbons in Colombian PETROLEA field. The second strategy is called "top-down strategy" and starts the solution using heuristic expert knowledge represented as rules, frames, objects in a knowledge base. As an example an inferential model for Enhanced oil recovery screening is designed from bibliographic information and adapted to Colombian oil fields.

In this paper we present a methodology for the automatic recognition of black Sigatoka in commercial banana crops. This method uses a LeNet convolutional neural network to detect the progress of infection by the disease in different regions of a leaf image; using this information, we trained a decision tree in order to classify the level of infection severity. The methodology was validated with an annotated database, which was built in the process of this work and which can be compared with other state-of-the-art alternatives. The results show that the method is robust against atypical values and photometric variations.