Fracture detection from water saturation log data using a Fourier-wavelet approach

Fracture detection as applied to reservoir characterization is a key step towards modeling of fracturedreservoirs. While different methods have been proposed for detection and characterization of fractures and fractured zones, each is associated with certain shortcomings that prevent from their full use in different related engineering application environments. In this paper a new method is proposed for detection of fractured zones and fracture density in which water saturation log data is utilized. For detection of fractures, we have used wavelet transform and properties of wavelets that are highly suitable for detection of changes and local features of data. To choose the optimum mother wavelet, we have used energy matching strategy in which a wavelet with the highest energy match between spectral energy of the signal at the dominant frequency band and the coefficient energy at the same band of wavelet decomposition of the signal is selected. We have used wavelet packet for a more narrow frequency band selection and enhanced results. Decomposing the water saturation data using wavelets showed that the majority of information of theoriginal log is hidden at low frequency bands. As a result, approximated section of wavelet transform of data was used for fracture detection, while shale volume (or gamma ray) log data was used to filter part of the errors in prediction and identification of the uncertain zones. This increased the accuracy of the results by 70%. Finally, a linear relation was derived between energy of approximated section of water saturation log and fracture density, allowing us to estimate the number of fractures in each fractured zone. The method was applied to four wells belonging to one of the Iranian oilfields located in the southwest region of the country and the results are promising. The use of large volume of data and the subsequent analysis increased the generalization ability of the proposed method

The impact of poor cementing casing damage: A numerical simulation study

A good knowledge of the parameters causing casing damage is critically important due to vital role of casing during the life of a well. Cement sheath, which fills in the gap between the casing and wellbore wall, has a profound effect on the resistance of the casing against applied loads. Most of the empirical equations proposed to estimate the collapse resistance of casing ignore the effects of the cement sheath on collapse resistance and rather assume uniform loading on the casing. This paper aims to use numerical modeling to show how a bad cementing job may lead to casing damage. Two separate cases were simulated where the differences between good and bad cementation on casing resistance were studied. In both cases, the same values of stresses were applied at the outer boundary of the models. The results revealed that a good cementing job can provide a perfect sheath against the tangential stress induced by far-field stresses and reduce the chance of casing to be damaged

A simple genetic algorithm for calibration of stochastic rock discontinuity networks

Este artículo propone un método para llevar a cabo la calibración de las familias de discontinuidades en macizos rocosos. We present a novel approach for calibration of stochastic discontinuity network parameters based on genetic algorithms (GAs). To validate the approach, examples of application of the method to cases with known parameters of the original Poisson discontinuity network are presented. Parameters of the model are encoded as chromosomes using a binary representation, and such chromosomes evolve as successive generations of a randomly generated initial population, subjected to GA operations of selection, crossover and mutation. Such back-calculated parameters are employed to make assessments about the inference capabilities of the model using different objective functions with different probabilities of crossover and mutation. Results show that the predictive capabilities of GAs significantly depend on the type of objective function considered; and they also show that the calibration capabilities of the genetic algorithm can be acceptable for practical engineering applications, since in most cases they can be expected to provide parameter estimates with relatively small errors for those parameters of the network (such as intensity and mean size of discontinuities) that have the strongest influence on many engineering applications

Using Bacillus Cereus as a Geo-Biological Marker For Gold Prospecting in Iran

Several methods have been developed for gold exploration in the past, among which biological base method is known to be the most efficient with least expenses. This method can also be used for latent gold prospects exploration. In the present study, the possibility of applying Bacillus cereus frequency in soil as a biological marker was investigated for the exploration of latent gold prospecting in Iran. The study was performed on three gold sources in Iran known as Moteh, Zarmehr and Mahallat, however, the major focus was on Mahallat gold reserve. The results of bacterial cultivation showed that no bacteria have been isolated in samples taken from Moteh soil. On the other hand, the presence of bacteria was observed in cultural media which were prepared from the collected samples from Zarmehr and Mahallat.In Mahallat gold reserve the frequency of bacteria was noticeable, particularly in the soils with in-situ fine-grained. In addition, it was seen that when the gold grad increases the bacterial frequency of Bacillus cereus will also increase. Finally, a linear correlation was developed between bacterial frequency and the gold semi-quantified grad. Using this correlation the monitoring of semi-quantified gold grad can be undertaken

Improvement of Fracture Network Modeling in Fractured Reservoirs Using Conditioning and Geostatistical Method

The fracture network in hydrocarbon reservoirs plays a major role in reservoir fluid transfer to production wells. Modeling of fracture in fractured reservoir is often done randomly. Modelling is based on image logs and core information. Because the information is available in a small number of wells, the model is not reliable and this problem makes it impossible to predict the correct flow rate and the amount of wells produced. In this study, an algorithm based on primary and secondary data for fracture network modelling in one of the southwest fields of Iran has been presented. The initial data include aperture fracture and fracture density, and secondary data includes petrophysical data, i.e. electrical resistance and resistance logs used to scale-up characteristics of fracture in wells. In this study, we tried to increase the accuracy of modelling by using modelling conditionality on existing and constructed data. Gaussian conditional simulation produces a set of realizations on which non-linear statistics can be readily available. In this way, information was entered into the model in areas where fracture was predicted to exist. Using the turning bands co-simulation method in geostatistic, the fracture characteristics were simulated in wells that were not available. Using the results of the 3D model, the fracture of the reservoir was re-constructed. The results showed that the modelling performed in this study has been able to increase the fracture prediction accuracy and their properties in fracture density by about 9% and in the fracture opening by about 5%

Badanie wpływu właściwości skał na prędkość wiercenia przy zastosowaniu metod statystycznych i inteligentnych: studium przypadku: szyb naftowy w południowo-zachodniej części Iranu

Rate of penetration (ROP) is one of the key indicators of drilling operation performance. The estimation of ROP in drilling engineering is very important in terms of more accurate assessment of drilling time which affects operation costs. Hence, estimation of a ROP model using operational and environmental parameters is crucial. For this purpose, firstly physical and mechanical properties of rock were derived from well logs. Correlation between the pair data were determined to find influential parameters on ROP. A new ROP model has been developed in one of the Azadegan oil field wells in southwest of Iran. The model has been simulated using Multiple Nonlinear Regression (MNR) and Artificial Neural Network (ANN). By adding the rock properties, the estimation of the models were precisely improved. The results of simulation using MNR and ANN methods showed correlation coefficients of 0.62 and 0.87, respectively. It was concluded that the performance of ANN model in ROP prediction is fairly better than MNR method.Prędkość wiercenia jest jednym z podstawowych parametrów charakteryzujących tempo prac wiertniczych. Oszacowanie prędkości wiercenia jest zagadnieniem kluczowym dla inżynierów wiertnictwa, gdyż pozwala na dokładne określenie czasu trwania prac, a co za tym idzie także kosztów operacyjnych. Szacowanie prędkości wiercenia odbywa się na podstawie modelu uwzględniającego parametry pracy oraz parametry środowiskowe. Pierwszy krok obejmuje pozyskanie danych o fizycznych i mechanicznych właściwościach skał na podstawie profilowania geofizycznego otworu. Zastosowano korelację odpowiednich par danych dla pokreślenie wpływu głównych czynników warunkujących prędkość wiercenia. Nowy model obliczania prędkości wiercenia opracowany został w okręgu naftowym Azadegan w południowo-zachodniej części Iranu. Symulacje prowadzono w oparciu o metodę wielokrotnej regresji nieliniowej a także przy wykorzystaniu sztucznych sieci neuronowych. Poprzez dodanie danych o właściwościach skał, model został znacznie udoskonalony. Wyniki symulacji prowadzonych w oparciu o powyższe metody wykazały współczynniki korelacji na poziomie 0.62 i 0.87. Stwierdzono, że metoda wykorzystująca sztuczne sieci neuronowe daje dokładniejsze szacunki prędkości wiercenia niż podejście bazujące wyłącznie na metodzie obliczania regresji nieliniowe