Papers by Mohammad Mohammadi Behboud
Journal of Petroleum Exploration and Production Technology
The geomechanical characteristics of a drill formation are uncontrollable factors that are crucia... more The geomechanical characteristics of a drill formation are uncontrollable factors that are crucial to determining the optimal controllable parameters for a drilling operation. In the present study, data collected in wells drilled in the Marun oilfield of southwestern Iran were used to develop adaptive network-based fuzzy inference system (ANFIS) models of geomechanical parameters. The drilling specific energy (DSE) of the formation was calculated using drilling parameters such as weight-on-bit (WOB), rate of penetration (ROP), rotational speed of drilling string (RPM), torque, bit section area, bit hydraulic factor, and bit hydraulic power. A stationary wavelet transform was subsequently used to decompose the DSE signal to the fourth level. The approximation values and details of each level served as inputs for ANFIS models using particle swarm optimization (PSO) algorithm and genetic algorithm (GA). As model outputs, the Young’s Modulus, uniaxial compressive strength (UCS), cohesio...
Journal of Petroleum Science and Engineering, 2018
A comprehensive knowledge of geomechanical characteristics of the formations above a reservoir (c... more A comprehensive knowledge of geomechanical characteristics of the formations above a reservoir (cap rock, in particular) can prevent a large spectrum of problems during drilling operation. Furthermore, access to such information during drilling operation greatly contributes to proper adjustment of controllable parameters and enhanced rate of penetration (ROP). Unfortunately, due to the high costs associated with the acquisition of petrophysical logs, these sets of data are commonly acquired within the reservoir interval only, i.e. at most 20% of total drilled length. As such, the present contribution is an attempt to use the concept of mechanical specific energy (MSE) to estimate geomechanical parameters of rock. For this purpose, data was collected from two vertical wells drilled into an oilfield in southwest of Iran. Firstly, based upon available drilling reports and Tukey method, outlier data points were detected and omitted. Then, MSE was evaluated using different models proposed for this purpose previously. The evaluation results indicated that, the results obtained from Dupriest and Keoderitz (2005) model were in good agreement with actual conditions at the considered wells. Therefore, the obtained MSE from this model along with flow rate (FR), bit tooth wear (CT), and Depth logs were used as input for multivariate nonlinear regression (MNLR) method as well as multi-layer perceptron neural network combined with cuckoo optimization algorithm (MLP-COA) and also with particle swarm optimization (MLP-PSO) to estimate confined compressive strength (CCS), uniaxial compressive strength (UCS), internal friction angle (ϕ), and Poisson's ratio (ν). Models were trained on data from one well and then validation-tested on the other well's data. Results of adopting these models indicated that, as far as the estimation of geomechanical parameters was concerned, intelligent models were of higher accuracy and reliability than the regression model. A comparison between the results of MLP-COA and MLP-PSO models showed that, COA outperforms PSO algorithm in achieving a model of higher accuracy and reliability. Results of the three models indicated that, the presented method in this research possesses large potentials for estimating CCS, UCS, and ϕ parameters, and it can be stipulated with certainty that, the proposed method can be used to estimate the parameters at other wells across the field under study provided further data is available from more wells and even the formations overlying the reservoir. However, application of this method for estimating ν shall be practiced with care.
Journal of Petroleum Science and Engineering, 2022
Accurate prediction of pore pressure (PP) is among the most critical concerns to the design of dr... more Accurate prediction of pore pressure (PP) is among the most critical concerns to the design of drilling operation because of the remarkable role of this parameter in preventing particular drilling problems such as wellbore instability, drilling pipe stuck, mud loss, kicks, and even blow outs. Given the limitations of PP measurement through in-hole well tests, a number of analytic and intelligent techniques have been developed to estimate the PP from conventionally available petrophysical logs at offset wells. In this contribution, analytic equations are combined with intelligent algorithms (IAs) in an integrated workflow for estimating the PP. For this purpose, we collected the required data from two wells (herein referred to as Wells A and B) penetrating a carbonate reservoir in two fields in southwestern Iran. The collected data included full-set petrophysical log data at a total of 2850 points as well as 15 measured PPs using the RFT tool. In order to model and validate the results, the data from Well A was used to train the model, with the Well-B data used for validation. Once finished with data collection, a noise attenuation stage was implemented through median filtering. Subsequently, PP estimation was practiced using a couple of popular analytic models, namely modified Eaton's, Bowers', and compressibility models, with the results compared to the measured PPs. Next, a feature selection phase was conducted where depth (Depth), gamma ray log (CGR), density log (RHOB), resistivity log (RT), pore compressibility (Cp), and slowness log (DT) were selected as the most effective parameters for estimating the PP out of the 8 parameters studied at Well A. Feature selection was performed using the second version of nondominated-sorting genetic algorithm (NSGA-II) combined with multilayer perceptron (MLP) neural network (NN). Next, deep learning techniques, simple form of the least square support vector machine (LSSVM) and its hybrid forms with particle swarm optimization (PSO), cuckoo optimization algorithm (COA), and genetic algorithm (GA), and multilayer extreme learning machine (MELM) hybridized with the PSO, COA, and GA were used to estimate the PP based on the data at Well A, with the results then validated using the data at Well B. Results of the training and testing phases showed that, among the 9 models considered in this research, the best results were produced by the CNN model followed by MELM-COA, and LSSVM-COA, corresponding to root-mean-square errors (RMSEs) of 0.1072, 0.1175, and 0.1237 and determination coefficients (R2) of 0.9884, 0.9860, and 0.9844, respectively, indicating the higher accuracy and generalizability of these models compared to other investigated models. Evaluation of these models on the validation data from Well B further remarked the superiority of the CNN model, as per an RMSE and R2 of 0.1066 and 0.9806, respectively. Indeed, the better performance of the CNN model than the other models in both the training and validation phases reflects the high generalizability of this model in the range of the studied data. In general, the good performance of the intelligent models in similar formation along two wells – where the analytic models rather failed to exhibit consistently good performance – proves the superiority of the IAs over conventional analytic models. This methodology is strongly recommended provided more diverse data is available at in larger amounts.
Multiplicity of the effective factors in drilling reflects the complexity of the interaction betw... more 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.
Conference Presentations by Mohammad Mohammadi Behboud
Throughout the history Petroleum drilling of wells, one of the most important problems of this in... more Throughout the history Petroleum drilling of wells, one of the most important problems of this industry is the lack of speed of drilling deep down. 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. Acoustic logs role in the process of reservoir characterization is undeniable role. The pressure wave speed and shear wave are used in determining the geo-mechanical parameters and type of formation lithology. In this article using the ANFIS – PSO algorithm, the relationship between rock specific energy with shear wave and pressure wave log is achieved. Using drilling parameters corresponding drilling specific energy (DSE) to formation was calculated. Special energy used as input in the wavelet function, the DSE signal was decomposed to 4th level using a db1 wavelet function. After analysis, wavelet function output used as inputs ANFIS-PSO network. Results have shown that the combination of ANFIS-PSO compared to ANFIS-GA is a higher predicted. So that the corresponding mean square error (MSE) and correlation coefficient to this model were found to be 5.5 and 0.74, pressure wave, 7.7 and 0.68, for shear wave, respectively. The proposed method can provide valuable information on pressure wave speed and shear wave in the absence of petrophysical logs.
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Papers by Mohammad Mohammadi Behboud
Conference Presentations by Mohammad Mohammadi Behboud