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

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

ODABIR DLIJETA ZA BUŠENJE STIJENA S RAZLIČITIM TALOŽNIM FACIJESIMA UPORABOM METODE MARKOVLJEVA LANCA: PRIMJER STUDIJE NAFTNOGA POLJA U JUŽNOME IRANU

The selection of a drill bit is an essential issue in well planning. Furthermore, identification and evaluation of sedimentary rocks before well drilling plays a crucial role in choosing the drill bit. Moreover, the Markov chain as a stochastic model is one of the powerful methods for identifying lithological units, which is based on the calculation of the transition probability matrix or transition matrix. The Markov chain experiences transitions from one state (a situation or set of values) to another according to specified probabilistic rules. In this paper, the Markov chain was implemented for bit selection in a formation with different sedimentary facies (such as the Dashtak Formation). Therefore, the proper drill bit was proposed by utilizing the transition matrix of rock facies and the available bits. This process was carried out in two wells where the thicknesses of the Dashtak Formation are 960 meters and 1410 meters. Consequently, the results indicate that the Markov chain is a practical method for selecting bits in a sequence of rock facies based on an acceptable matching between the reality mode (the used bits in the well) and the Markov chain results. Besides, in the case of using an improper bit in a well, and using a bit in a washing and reaming operation, there were differences between the used bits and the Markov chain outputs.Odabir dlijeta iznimno je važan korak u izradi projekta bušotine, a prepoznavanje i procjena taložnih stijena prije početka bušenja ima presudnu ulogu u odabiru dlijeta. Markovljev lanac, kao primjer stohastičkoga modela, jedna je od važnijih metoda za razlikovanje litoloških jedinica. Temelji se na računu matrice vjerojatnosti prijelaza ili matrice prijelaza. Markovljev lanac opisuje prijelaze iz jednoga stanja (situacije ili skupa vrijednosti) u drugo prema određenim vjerojatnosnim pravilima. U ovome je radu prvi put opisana uporaba Markovljeva lanca kod odabira dlijeta za bušenje kroz interval s različitim taložnim facijesima (formacija Dashtak). Stoga su u odabiru odgovarajućega dlijeta korišteni prijelazna matrica stijenskih facijesa i podatci o dostupnim dlijetima. Ovaj postupak proveden je u dvjema bušotinama gdje su debljine formacije Dashtak 960 i 1410 m. Dobiveni rezultati pokazuju da je Markovovljev lanac praktična metoda za odabir dlijeta kod bušenja niza litofacijesa. Zaključak je donesen na temelju stupnja podudaranja između stvarnih podataka (dlijeta korištenih u bušotini) i rezultata dobivenih modelom (Markovljevim lancem). Također, u slučaju uporabe neodgovarajućega dlijeta u bušotini za operacije pročišćavanja i proširenja kanala bušotine pojavile su se razlike između korištenih dlijeta i rezultata dobivenih Markovljevim lancem

PROCJENA UTJECAJA GEOMETRIJE PUKOTINA NA PROPUSNOST, PRIMJER LABORATORIJSKOGA ISPITIVANJA I NUMERIČKOGA MODELIRANJA

The geometry of fractures includes orientation, spacing, aperture are among the parameters affecting permeability in rocks. Studying the effect of fractures geometry on the permeability in a laboratory scale requires the selection of a suitable sample in terms of physical and mechanical properties. Therefore, in this study, fibrous fiber was selected due to low water absorption and permeability as well as its non-brittle behavior and flexibility. In order to investigate the effect of fracture geometry on the permeability, 1, 2, 3, and 4 fractures with spacing greater than 50 mm, 50 mm, 25 mm, and 15 mm and with orientations of 0, 15, 30, 45, and 60 degrees to the horizon in the sample were created. The fractures did not come into contact with the surface of the sample .The results showed that the permeability raises exponentially with increasing orientation and decreasing the spacing. This situation is mostly seen in fractures with orientations larger than 30 degrees. Also, the permeability measured in the laboratory was compared with the results obtained from the numerical method of distinct elements and UDEC software. The results showed an error of about 10-15%, which is well-matched between the permeability obtained from the laboratory and the numerical method.Geometrija pukotina uglavnom obuhvaća orijentaciju, razmak i promjer, kao varijable koje određuju propusnost stijena. Studije koje se bave izučavanjem geometrije pukotina na laboratorijskim uzorcima imaju preduvjet odabira prikladnih uzoraka za ocjenu fizičkih i mehaničkih svojstava. Stoga je ovdje odabrano vlakno s malom apsorpcijom vode i propusnošću te sa svojstvima nekrtosti, odnosno savitljivosti. Ispitane su 1, 2, 3 i 4 pukotine s razmacima većim od 50 mm, od 50 mm, od 25 i od 15 mm te orijentacijama od 0, 15, 30, 45 i 60 stupnjeva od vodoravne ravnine. Pukotine nisu bile u dodiru s površinom uzorka. Propusnost je rasla eksponencijalno, prateći porast kuta orijentacije i smanjivanje razmaka među pukotinama. To je najbolje opaženo s pukotinama pod kutom većim od 30 stupnjeva. Propusnost dobivena laboratorijski uspoređena je s rezultatima izmjerenim metodom konačnih elemenata i programom UDEC. Dobivena je pogrješka od 10 do 15 % čime je dokazano dobro podudaranje laboratorijskih i numeričkih rezultata

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 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

A New Approach towards Precise Planar Feature Characterization Using Image Analysis of FMI Image: Case Study of Gachsaran Oil Field Well No. 245, South West of Iran

Formation micro imager (FMI) can directly reflect changes of wall stratums and rock structures. Conventionally, FMI images mainly are analyzed with manual processing, which is extremely inefficient and incurs a heavy workload for experts. Iranian reservoirs are mainly carbonate reservoirs, in which the fractures have an important effect on permeability and petroleum production. In this paper, an automatic planar feature recognition system using image processing was proposed. The dip and azimuth of these features are detected using this algorithm to identify more precise permeability and the career of fluid in reservoirs. The proposed algorithm includes three main steps; first, pixels representing fractures are extracted from projected FMI image into location matrices x and y and the corresponding value matrix f(x, y). Then, two vectors X and Y as the inputs of CFTOOL of MATLAB are produced by the combination of these three matrices. Finally, the optimum combination of sine function is fitted to the sine shape of pattern to identify the dip and azimuth of the planar feature. The system was tested with real interpretation FMI rock images. In the experiments, the average recognition error of the proposed system is about 0.9% for the azimuth detection and less than 3.5% for the dip detection and the correlations between the actual dip and azimuth with the determined cases are more than 90% and 97% respectively. Moreover, this automatic system can significantly reduce the complexity and difficulty in the planar feature detection analysis task for the oil and gas exploration.</span

Displacement Discontinuity Analysis of the Effects of Various Hydraulic Fracturing Parameters on the Crack Opening Displacement (COD)

The combination of horizontal drilling along with hydraulic fracturing has significantly improved the production of hydrocarbon reservoirs and made it possible to extract the relatively impermeable and uneconomical reservoirs. The production rate of oil and gas wells increases proportional to hydraulic fracture aperture or crack opening displacement (COD). This is an important parameter in fracture mechanics literature and hydraulic fracturing of hydrocarbon reservoirs. Despite the significance of COD there are a few analytical solutions for the estimation of COD under certain conditions. In this paper the effect of various parameters on COD is investigated semi-analytically. A higher order displacement discontinuity method is used to consider the effects of different parameters (Young’s modulus, Poisson’s ratio, internal pressure, maximum and minimum horizontal stresses, crack half-length and its inclination with maximum horizontal stress) on the COD in a hydraulic fracturing process under arbitrarily conditions. The coefficient of determination and standard error of the estimate were 94.35% and 4.37×10-4 respectively, showing a good agreement between the fitted equation and the numerical results. The effect of propagation and well radius on the maximum COD was also investigated. The results showed that COD increases almost linearly with the crack propagation and increase of well radius of hydraulic fractures (HFs). These effects are more significant when HFs are propagating in the direction of maximum horizontal stress. The proposed equation and the results from propagation of hydraulic fractures can be used in early stages of a hydraulic fracturing design</span

Enhanced velocity based pore pressure prediction using lithofacies clustering: A case study from a reservoir with complex lithology in Dezful Embayment, SW Iran

The primary goal of this paper is to improve accuracy and reliability of the conventional Bowers and Tau methods in a reservoir with complex lithology. We demonstrate the capability of the proposed method through a case study in a reservoir in the Southwest of Iran. Velocity based pore pressure prediction methods are widely accepted as a routine technique in the petroleum industry. Despite recent improvements, still, literature suffer from inconsistencies and uncertainties mostly arise from velocity anomalies due to complex lithostratigraphic setting or presence of various formation fluids. Our proposed workflow aims to address those issues and improve the accuracy of the estimations by clustering the input data into zones with specific geomechanical characteristics. We hypothesis each major zones at the offset test wells might have distinct "Normal Compaction Trend" with a different empirical constants. Thus, Bowers and Tau methods should be calibrated for each cluster rather the whole stratigraphic column. The clustering task was done by statistical analyses of a suite of well logs and validated with core derived lithologies. Several clustering techniques namely K-means, basic sequential algorithmic scheme, single, and complete linkage hierarchical were applied and compared to find the best algorithm. We found that the self-organizing map (SOM) method provide the best results by maximizing lithology likelihood within each cluster and improve the efficiency of the Bowers and Tau methods. Satisfactory results of this study offer a safe ground for implementation of the proposed method in other sedimentary basins