Exploitation
M. Bavand Savadkoohi; B. Tokhmechi; E. Gloaguen; A.R. Arab-Amiri
Abstract
Computer graphics offer various gadgets to enhance the reconstruction of high-order statistics that are not correctly addressed by the two-point statistics approaches. Almost all the newly developed multiple-point geostatistics (MPS) algorithms, to some extent, adapt these techniques to increase the ...
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Computer graphics offer various gadgets to enhance the reconstruction of high-order statistics that are not correctly addressed by the two-point statistics approaches. Almost all the newly developed multiple-point geostatistics (MPS) algorithms, to some extent, adapt these techniques to increase the simulation accuracy and efficiency. In this work, a scrutiny comparison between our recently developed MPS algorithm, the cross-correlation-wavelet simulation (CCWSIM), and a well-known MPS algorithm, FILTERSIM, is performed. The main motivation to benchmark these two algorithms is that both exploit some digital image processing filters for feature extraction. Indeed, both algorithms compute the similarity (or dissimilarity) between data events in simulation grid and training image in the feature space. In order to compare the accuracy of the algorithms, some statistics such as facies proportion, variogram, and connectivity function are computed. The results obtained reveal an excellent agreement of the CCWSIM realizations with the training image rather than FILTERSIM. Furthermore, on average, the required simulation runtime for CCWSIM is at least 10 times less than that for FILTERSIM.
Exploitation
M. Jahangiri; Seyed R. Ghavami Riabi; B. Tokhmechi
Abstract
Bearing in mind that lack of data is a common problem in the study of porphyry copper mining exploration, our goal was set to identify the hidden patterns within the data and to extend the information to the data-less areas. To do this, the combination of pattern recognition techniques has been used. ...
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Bearing in mind that lack of data is a common problem in the study of porphyry copper mining exploration, our goal was set to identify the hidden patterns within the data and to extend the information to the data-less areas. To do this, the combination of pattern recognition techniques has been used. In this work, multi-layer neural network was used to estimate the concentration of geochemical elements. From 1755 surface and boreholes data available, analyzed by ICP, 70% was used for training, and the rest for testing. The average accuracy of estimators for 22 geochemical elements when using all data was equal to 75%. Based on validation, the optimal number of clusters for the total data was identified. The Gustafson-Kessel (GK) clustering was used to design the estimator for the geochemical element concentrations in different clusters, and the clusters were selected for estimation. The results obtained show that using GK, the estimator's average accuracy increase up to 84%. The accuracy of the elementsZn, As, Pb, Mo, and Mn with low accuracies of 0.51, 0.62, 0.64, 0.65, and 0.68 based on all data were developed to 0.76, 0.86, 0.76, 0.80, and 0.71 with the clustered data, respectively. The mean square error using all the data was 0.079, while in the case of hybrid developed method, it decreased to 0.048. There were error reductions in Al from 0.022 to 0.012, in As, from 0.105 to 0.025, and from 0.115 to 0.046 for S.
Exploitation
R. Ghasemi; B. Tokhmechi; G. Borg
Abstract
The known ore deposits and mineralization trends are important key exploration criteria in mineral exploration within a specific region. Fry analysis has conventionally been considered as a suitable method to determine the mineralization trends related to linear structures. Based upon literature sources, ...
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The known ore deposits and mineralization trends are important key exploration criteria in mineral exploration within a specific region. Fry analysis has conventionally been considered as a suitable method to determine the mineralization trends related to linear structures. Based upon literature sources, to date, no investigation has been carried out that includes the Sensitivity Analysis of Feature's Number (SAFN), Sensitivity Analysis of Window Size (SAWS), and Sensitivity Analysis of Spatial Distribution (SASD) of Fry analysis related to mineral locations. In this work, SAFN, SAWS, and SASD are performed by moving several different sub-windows among the main window in order to identify the main trends of mineralization by Fry analysis in the Bavanat region of Iran, which is qualified by its regional and local faults pattern. Based upon our investigation, the effectiveness of the window size and the number of features on Fry analysis are 15-30%. The determined main trends of sub-windows increase, whereas its distribution function of Fry outputs is more similar to the distribution function of Fry outputs of the main window. Moreover, the directions of rose diagrams could be changed due to the edge effects of marginal features around the selected window. However, by selecting an appropriate window, this problem can be solved. Additionally, by an appropriate window selection, the most suitable regional situation is an area that contains the largest number of deposits with a similar metallogenetic origin. Based upon our investigation, the distribution function of the Fry outputs is the main factor that directly controls the identified mineralization pattern of the selected windows.
M. Anemangely; A. Ramezanzadeh; B. Tokhmechi
Abstract
Achieving minimum cost and time in reservoir drilling requires evaluating the effects of the drilling parameters on the penetration rate and constructing a drilling rate estimator model. Several drilling rate models have been presented using the drilling parameters. Among these, the Bourgoyne and Young ...
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Achieving minimum cost and time in reservoir drilling requires evaluating the effects of the drilling parameters on the penetration rate and constructing a drilling rate estimator model. Several drilling rate models have been presented using the drilling parameters. Among these, the Bourgoyne and Young (BY) model is widely utilized in order to estimate the penetration rate. This model relates several drilling parameters to the penetration rate. It possesses eight unknown constants. Bourgoyne and Young have suggested the multiple regression analysis method in order to define these constants. Using multiple regressions leads to physically meaningless and out of range constants. In this work, the Cuckoo Optimization Algorithm (COA) is utilized to determine the BY model coefficients. To achieve this goal, the corresponding data for two wells are collected from one of the oilfields located in SW of Iran. The BY model constants are determined individually for two formations in one of the wells. Then the determined constants are used to estimate the drilling rate of penetration in the other well having the same formations. To compare the results obtained for COA, first, the two mathematical methods including progressive stochastic and multiple regressions were implemented. Comparison between these methods indicated that COA yields more accurate and reliable results with respect to the others. In the following, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as meta-heuristic algorithms were applied on the field data in order to determine BY model’s coefficients. Comparison between these methods showed that the COA has fast convergence rate and estimation error less than others.
M. Mohammadi Behboud; A. Ramezanzadeh; B. Tokhmechi
Abstract
Multiplicity of the effective factors in drilling reflects the complexity of the interaction between rock mass and drilling bit, which is followed by the dependence of parameters and non-linear relationships between them. Rock mass or, in other words, the formation intended for drilling, as the drilling ...
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Multiplicity of the effective factors in drilling reflects the complexity of the interaction between rock mass and drilling bit, which is followed by the dependence of parameters and non-linear relationships between them. Rock mass or, in other words, the formation intended for drilling, as the drilling environment, plays a very essential role in the drilling speed, depreciation of drilling bit, machines, and overall drilling costs. Therefore, understanding the drilling environment and the characteristics of the in-situ rock mass contributes a lot to the selection of the machines. In this work, a 1D geo-mechanical model of different studied wells is built by collecting the geological data, well logs, drilling data, core data, and pressure measurements of the formation fluid pressure in various wells. Having the drilling parameters of each part of the formation, its specific energy is calculated. The specific energy index can be used for predicting the amount of energy consumed for drilling. In order to find the relationship between the drilling specific energy (DSE) and its effective parameters, the multivariate regression model is used. Modeling DSE is done using the multivariate regression, which contains the parameters rock characteristics, well logs, and a combination of these two features. 70% and 30% of the data are, respectively, selected as the training and test for validation. After analyzing the model, the correlation coefficients obtained for the training and test data were, respectively, found to be 0.79 and 0.83. The parameters uniaxial compressive strength (UCS), internal friction angle, and fluid flow are among the most important factors found to affect DSE.
R. Vahedi; B. Tokhmechi; M. Koneshloo
Abstract
We use a multi-resolution analysis based on a wavelet transform to upscale a 3D fractured reservoir. This paper describes a 3D, single-phase, and black-oil geological model (GM) that is used to simulate naturally-fractured reservoirs. The absolute permeability and porosity of GM is upscaled by all the ...
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We use a multi-resolution analysis based on a wavelet transform to upscale a 3D fractured reservoir. This paper describes a 3D, single-phase, and black-oil geological model (GM) that is used to simulate naturally-fractured reservoirs. The absolute permeability and porosity of GM is upscaled by all the possible combinations of Haar, Bior1.3, and Db4 wavelets in three levels of coarsening. The applied upscaling method creates a non-uniform computational grid, which preserves its resolved structure in the near-well zones as well as in the high-permeability sectors but the data are scaled up in the other regions. To demonstrate the accuracy and efficiency of the method, the values for the oil production rate, mean reservoir pressure, water cut, and total amount of water production are studied, and their mean error is estimated for the upscaled models. Finally, the optimized model is selected based on the computation time and accuracy value.
M. Hemmatian; B. Tokhmchi; V. Rasouli; R. Gholami
Abstract
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 ...
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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.
Amir Mollajan; Hossein Memarian; Behzad Tokhmechi
Abstract
Detection of Oil-Water Contacts (OWCs) is one of the primary tasks before evaluation of reservoir’s hydrocarbon in place, determining net pay zones and suitable depths for perforation operation. This paper introduces Bayesian decision making tool as an effective technique in OWC detecting using ...
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Detection of Oil-Water Contacts (OWCs) is one of the primary tasks before evaluation of reservoir’s hydrocarbon in place, determining net pay zones and suitable depths for perforation operation. This paper introduces Bayesian decision making tool as an effective technique in OWC detecting using wire line logs. To compare strengths of the suggested method in detecting OWC with conventional one, the same database was used. Proposed method was applied to wire line logs in three wells of a carbonate reservoir in an oil field of the southwestern Iran and its results have been evaluated by well testing results. Results indicate that the usage of Bayesian method in detecting OWC is more accurate than conventional method and may improve the results about 5% on average. In addition, using this method, any variation of water saturation (Sw) log and reservoir fluid types may be detectable.
Mostafa Javid; Behzad Tokhmechi
Abstract
There are two methods for identifying formation interface in oil wells: core analysis, which is a precise approach but costly and time consuming, and well logs analysis, which petrophysists perform, which is subjective and not completely reliable. In this paper, a novel coupled method was proposed to ...
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There are two methods for identifying formation interface in oil wells: core analysis, which is a precise approach but costly and time consuming, and well logs analysis, which petrophysists perform, which is subjective and not completely reliable. In this paper, a novel coupled method was proposed to detect the formation interfaces using GR logs. Second approximation level (a2) of GR log gained from optimum mother wavelet decomposition was used for formation interface detection. Short time Fourier transform (STFT) of a2 was gained since the window band was fixed in the entire of well depths. Inverse STFT of various windows of transformed data was gained, which creates various signals in depth domain. To this end, a novel formulation was developed to obtain modified signal for formation interface detection. The mean of various resulted signals creates a smooth signal the logarithm well of which highlights formation interfaces. Synthetic data were used to test the applicability of proposed algorithm. Accordingly, GR logs corresponding to five different wells located in an oilfield in south of Iran also were used to investigate the accuracy and applicability of the proposed method. Lastly, the validation process took place by comparing the results of core data analysis and the proposed method. Good agreements were obtained between these approaches, demonstrating the applicability of the proposed methodology.
F. Khorram; H. Memarian; B. Tokhmechi; H. Soltanian-zadeh
Abstract
In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate ...
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In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate environment and processed. A total of 76 features were extracted from the identified rock samples in all images. Neural network used as an intelligent tool for ore grade estimation and the features of every image were combined with weighted average method. In order to feature dimensional decrease, principal component analysis method was used. Six principal components, which were extracted from the feature vectors, captured 90.661% of the total feature variance. Components were used as the input to neural network and four grade attributes of limestone (CaCO3, Al2O3, Fe2O3 and MgCO3) were used as the output. The root of mean squared error between the observed values and the model estimated values for the test data set are 6.378, 4.847, 0.1513 and 0.0284, the R2 values are 0.7852, 0.8663, 0.7591and 0.8094 for the mentioned chemical composition respectively. The magnitude of R2 indicates the correlation between actual and estimated data. Therefore, it can be inferred that the model can successfully estimate the limestone chemical compositions percentage.
