Net pay determination by artificial neural network: Case study on Iranian offshore oil fields

International audienceDetermining productive zones has always been a challenge for petrophysicists. On the other hand, Artificial Neural Networks are powerful tools in solving identification problems. In this paper, pay zone determination is defined as an identification problem, and is tried to solve it by trained Neural Networks. Proposed methodology is applied on two datasets: one belongs to carbonate reservoir of Mishrif, the other belongs to sandy Burgan reservoir. The results showed high precision in classifying productive zones in predefined classes with Classification Correctness Rate of more than 85% in both geological conditions. Applicability of proposed pay zone determination procedure in carbonate environment is a great advantage of developed methodology. Fuzzified output, being independent of core tests and verification with well tests results are of other advantages of Neural Network-based method of pay zone detection

Net pay determination by Dempster rule of combination: Case study on Iranian offshore oil fields

International audienceNet pay detection is a key stage in reservoir characterization for several purposes: reserve estimation, reservoir modeling and simulation, production planning, etc. Determining productive zones always is simultaneous with some amount of uncertainty due to lack of enough data, insufficiency of knowledge and wild-nature of petroleum reservoirs. It becomes even more challenging in carbonates, because of their highly heterogeneous environment. Conventionally, evaluating net pays is done by applying petrophysical cutoffs on well-logs, which results in crisp classification of pay or non-pay zones. In addition, cutoff based method is developed in sandstones, and does not provide suitable results in carbonates at all. Proposed methodology of this work, Dempster-Shafer Theory, is a generalization of Bayesian Theory of conditional probabilities. Net pays are studied in two oil reservoirs by this theory; one of them is carbonate reservoir of Mishrif, the other is sandy Burgan reservoir. For validation, results 2 are compared to well tests and output of conventional cutoff method. The advantages of using Dempster-Shafer Theory, comparing to conventional cutoff based method in studying net pays is: to have a continuous fuzzy output, based on geological facts, with high generalization ability and more compatibility with well test data

Developing a method for identification of net zones using log data and diffusivity equation

International audienceDistinguishing productive zones of a drilled oil well plays a very important role for petroleum engineers to decide where to perforate to produce oil. Conventionally, net pay zones are determined by applying a set of cutoffs on perophysical logs. As a result, the conventional method finds productive intervals crisply. In this investigation, a net index value is proposed, then; diffusivity equation is utilized to calculate the proposed index value. The new net determination method is applied on the interval of Sarvak Formation of two datasets of two nearby wells. The best advantage of this newly developed net determination method is its fuzzy output. Fuzzy net pay determination is valuable in grading pay zones and not classifying all productive zones in a single class. Another advantage of the proposed net determination method is its higher accuracy in identifying productive zones in comparison with cutoff based method

Identifying productive zones of the Sarvak formation by integrating outputs of different classification methods

International audienceSarvak formation is the second major carbonate reservoir in Iran. There are several geological, petrophysical and geophysical investigations which have been carried out on this important reservoir. In this work, Sarvak is studied to find productive zones. At first, four different methods were used to identify producing intervals from well log data and well test results. Then, final zoning is generated by integrating outputs of these four methods. One of them is the conventional cutoff based method; the other three methods are based on flow equation, Bayesian and fuzzy theories. Thereafter, by considering the classification correctness rate of each classifier in each well and technique of majority voting, a unique zoning for Sarvak formation is presented. Based on the final zoning, the whole Sarvak interval is divided into seven zones. Three of them are classified as oil producing zones, two of them cannot be classified as conventionally producing zones, and the remaining two are water producing. Zone number 2 not only has the highest production rate, but also is the most homogeneous zone among the productive zones. The novelty of this research is using well test results in defining productive classes, which improves the certainty of classification in comparison with previous works that were based on core analysis and log data

Application of fuzzy classifier fusion in determining productive zones in oil wells

International audienceThis study is an application of data fusion techniques, especially fuzzy theory, in determining oil producing zones through four nearby wells, located on an oil field in south west of Iran. Two fusing techniques, used here are based on Bayesian and fuzzy theories. At first, two Bayesian classifiers are being constructed by training in two different wells; then a fuzzy operator, called Sugeno discrete integral, is used to fuse outputs of two mentioned Bayesian classifiers. Finally, it is concluded that using fuzzy classifier fusion improves not only certainty and confidence of decision making, but also generalization ability of determining productive zones

Application of Bayesian in determining productive zones by well log data in oil wells

International audienceExploration specialists conventionally utilize a cut-off-based method tofind productive zones inside the oil wells. Using conventional method, payzones are separated crisply from non-pay zones by applying cut-off values onsome petrophysical features.In this paper, a Bayesian technique is developed to find productivezones (net pays), and Bayesian Network is used to select the most appropriateinput features for this newly developed method. So, two Bayesian methodswere developed: the first one with conventional pay determination inputs(shale percent, porosity and water saturation), the other with two inputs,selected by Bayesian Network (porosity and water saturation). Twodeveloped Bayesian methods are applied on well log dataset of two wells:one well is dedicated for training and testing Bayesian methods, the other forchecking generalization ability of the proposed methods. Outputs of twopresented methods were compared with the results of conventional cut-offbasedmethod and production test results (i.e. a direct procedure to checkvalidation of proposed methods).Results show that the most prominent advantage of developedBayesian method is determination of net pays fuzzily with no need to identifycut-offs, in addition to higher precision of classification: nearly 30%improvement in precision of determining net pays of first well (training well),and about 50% improvement in precision of determining productive zonesthrough the generalizing well

Estimation of in place hydrocarbon volume in multilayered reservoirs using deterministic and probabilistic approaches

International audienceThis paper presents the results of the calculation techniques used for estimation of hydrocarbon initially in place for a multilayered gas reservoir located in the Persian Gulf via two methodologies. The porosity, water saturation, and net pay raw datasets for six wells enclosed within the studied area are thoroughly examined and fed to the deterministic and probabilistic calculation algorithms and the results are compared. In order to include the probable effects of the uncertainties associated with reservoir characterization, two distinct methodologies are developed and incorporated in both types of the calculation processes. In the first methodology, total hydrocarbon volume is calculated in one stage, while in another, hydrocarbon volume estimation have been carried out separately in each producing layer; then, summing them to estimate total hydrocarbon volume. The prominent conclusion of this research indicates that the second developed method in both deterministic and probabilistic conditions presentsmore reliable results for hydrocarbon volume estimation

Feature selection for reservoir characterisation by Bayesian network

International audienceThe more accurate feature identification, the more precise reservoir characterisation. Porosity, permeability and other rock properties could be estimated and classified by analytical and intelligent methods. Feature selection, plays a vital role in the process of identification. In this work, two goals are followed: first, developing Bayesian Network, K2 algorithm, as a complementary means (not an alternative) to find interrelationships of petrophysical parameters. Second, feature conditioning for estimating porosity and permeability, vug and fracture detection, and net pay determination. Due to the results, bulk density log is introduced as the most important feature for characterising the reservoir because it is found useful for identifying all the studied reservoir features

Application de l'approche hybride incertitude-partitionnement pour le prétraitement des données de diagraphie

In the subsurface geology, characterization of geological beds by well-logs is an uncertain task. The thesis mainly concerns studying vertical resolution of well-logs (question 1). In the second stage, fuzzy arithmetic is applied to experimental petrophysical relations to project the uncertainty range of the inputs to the outputs, here irreducible water saturation and permeability (question 2). Regarding the first question, the logging mechanism is modelled by fuzzy membership functions. Vertical resolution of membership function (VRmf) is larger than spacing and sampling rate. Due to volumetric mechanism of logging, volumetric Nyquist frequency is proposed. Developing a geometric simulator for generating synthetic-logs of a single thin-bed enabled us analysing sensitivity of the well-logs to the presence of a thin-bed. Regression-based relations between ideal-logs (simulator inputs) and synthetic-logs (simulator outputs) are used as deconvolution relations for removing shoulder-bed effect of thin-beds from GR, RHOB and NPHI well-logs. NPHI deconvolution relation is applied to a real case where the core porosity of a thin-bed is 8.4%. The NPHI well-log is 3.8%, and the deconvolved NPHI is 11.7%. Since it is not reasonable that the core porosity (effective porosity) be higher than the NPHI (total porosity), the deconvolved NPHI is more accurate than the NPHI well-log. It reveals that the shoulder-bed effect is reduced in this case. The thickness of the same thin-bed was also estimated to be 13±7.5 cm, which is compatible with the thickness of the thin-bed in the core box (<25 cm). Usually, in situ thickness is less than the thickness of the core boxes, since at the earth surface, there is no overburden pressure, also the cores are weathered. Dempster-Shafer Theory (DST) was used to create well-log uncertainty range. While the VRmf of the well-logs is more than 60 cm, the VRmf of the belief and plausibility functions (boundaries of the uncertainty range) would be about 15 cm. So, the VRmf is improved, while the certainty of the well-log value is lost. In comparison with geometric method, DST-based algorithm resulted in a smaller uncertainty range of GR, RHOB and NPHI logs by 100%, 71% and 66%, respectively. In the next step, cluster analysis is applied to NPHI, RHOB and DT for the purpose of providing cluster-based uncertainty range. Then, NPHI is calibrated by core porosity value in each cluster, showing low √MSE compared to the five conventional porosity estimation models (at least 33% of improvement in √MSE). Then, fuzzy arithmetic is applied to calculate fuzzy numbers of irreducible water saturation and permeability. Fuzzy number of irreducible water saturation provides better (less overestimation) results than the crisp estimation. It is found that when the cluster interval of porosity is not compatible with the core porosity, the permeability fuzzy numbers are not valid, e.g. in well#4. Finally, in the possibilistic approach (the fuzzy theory), by calibrating α-cut, the right uncertainty interval could be achieved, concerning the scale of the study.La thèse est principalement centrée sur l'étude de la résolution verticale des diagraphies. On outre, l'arithmétique floue est appliquée aux modèles expérimentaux pétrophysiques en vue de transmettre l'incertitude des données d'entrée aux données de sortie, ici la saturation irréductible en eau et la perméabilité. Les diagraphies sont des signaux digitaux dont les données sont des mesures volumétriques. Le mécanisme d'enregistrement de ces données est modélisé par des fonctions d'appartenance floues. On a montré que la Résolution Verticale de la Fonction d'Appartenance (VRmf) est supérieur d'espacement. Dans l'étape suivante, la fréquence de Nyquist est revue en fonction du mécanisme volumétrique de diagraphie ; de ce fait, la fréquence volumétrique de Nyquist est proposée afin d'analyser la précision des diagraphies. Basé sur le modèle de résolution verticale développée, un simulateur géométrique est conçu pour générer les registres synthétiques d'une seule couche mince. Le simulateur nous permet d'analyser la sensibilité des diagraphies en présence d'une couche mince. Les relations de régression entre les registres idéaux (données d'entrée de ce simulateur) et les registres synthétiques (données de sortie de ce simulateur) sont utilisées comme relations de déconvolution en vue d'enlever l'effet des épaules de couche d'une couche mince sur les diagraphies GR, RHOB et NPHI. Les relations de déconvolution ont bien été appliquées aux diagraphies pour caractériser les couches minces. Par exemple, pour caractériser une couche mince poreuse, on a eu recours aux données de carottage qui étaient disponibles pour la vérification : NPHI mesuré (3.8%) a été remplacé (corrigé) par 11.7%. NPHI corrigé semble être plus précis que NPHI mesuré, car la diagraphie a une valeur plus grande que la porosité de carottage (8.4%). Il convient de rappeler que la porosité totale (NPHI) ne doit pas être inférieure à la porosité effective (carottage). En plus, l'épaisseur de la couche mince a été estimée à 13±7.5 cm, compatible avec l'épaisseur de la couche mince dans la boite de carottage (<25 cm). Normalement, l'épaisseur in situ est inférieure à l'épaisseur de la boite de carottage, parce que les carottes obtenues ne sont plus soumises à la pression lithostatique, et s'érodent à la surface du sol. La DST est appliquée aux diagraphies, et l'intervalle d'incertitude de DST est construit. Tandis que la VRmf des diagraphies GR, RHOB, NPHI et DT est ~60 cm, la VRmf de l'intervalle d'incertitude est ~15 cm. Or, on a perdu l'incertitude de la valeur de diagraphie, alors que la VRmf est devenue plus précise. Les diagraphies ont été ensuite corrigées entre l'intervalle d'incertitude de DST avec quatre simulateurs. Les hautes fréquences sont amplifiées dans les diagraphies corrigées, et l'effet des épaules de couche est réduit. La méthode proposée est vérifiée dans les cas synthétiques, la boite de carottage et la porosité de carotte. L'analyse de partitionnement est appliquée aux diagraphies NPHI, RHOB et DT en vue de trouver l'intervalle d'incertitude, basé sur les grappes. Puis, le NPHI est calibré par la porosité de carottes dans chaque grappe. Le √MSE de NPHI calibré est plus bas par rapport aux cinq modèles conventionnels d'estimation de la porosité (au minimum 33% d'amélioration du √MSE). Le √MSE de généralisation de la méthode proposée entre les puits voisins est augmenté de 42%. L'intervalle d'incertitude de la porosité est exprimé par les nombres flous. L'arithmétique floue est ensuite appliquée dans le but de calculer les nombres flous de la saturation irréductible en eau et de la perméabilité. Le nombre flou de la saturation irréductible en eau apporte de meilleurs résultats en termes de moindre sous-estimation par rapport à l'estimation nette. Il est constaté que lorsque les intervalles de grappes de porosité ne sont pas compatibles avec la porosité de carotte, les nombres flous de la perméabilité ne sont pas valables