جستجوی مقالات مرتبط با کلیدواژه "well log data" در نشریات گروه "مهندسی شیمی، نفت و پلیمر"

Efforts have been dedicated to correlating pore throat sizes with petrophysical and geological parameters. Log data is helpful in examining and analyzing the degree of pore type, the presence of clay minerals as well as porosity and the density of the reservoir. This study aims to establish a relationship between pore throat sizes and well log data through regression analysis. Well-logging data are routinely accessible and can be compared with the core data.The research uses mercury injection capillary pressure, core samples, and well log data from three wells within a field in the central Persian Gulf region. Equations were developed to link data from various well logs and their combinations to pore throat sizes in the reservoir. To address with challenges, core plugs were categorized into more homogeneous groups using Winland and flow zone indicator rock typing methods. The Winland method revealed that equations within each rock type exhibited low R2 values due to significant porosity variations. Conversely, integrating data from all seven rock types yielded better fits as the diverse porosity values counterbalanced each other's effects. However, the flow zone indicator rock typing approach did not enhance results as it was designed for circular and cylindrical capillary tubes, making it less effective for developing complex equations in carbonate reservoirs. The findings underscore the significance of defining homogeneous units accurately, as this step is crucial for enhancing results and establishing robust relationships between pore throat sizes and well log data. By integrating data from various rock types and refining the approach to defining homogeneous units, the study demonstrates the potential for improving the accuracy and applicability of pore throat size predictions in carbonate reservoirs.

Shear wave velocity (Vs) is one of the key geomechanical parameters effective in the drilling of hydrocarbon reservoirs. In this study, a novel machine learning (extra learning machine (ELM)) approach is developed to predict Vs based on four input variables obtained from well log, including neutron porosity (NPHI), bulk density (RHOB) and gamma-ray (GR). Two algorithms multi-layer perceptron (MLP) and ELM and various empirical equations (Brocher, Eskandari et al., Castagna et al. and Pickett) have been used to predict Vs in this paper. The results show that the performance accuracy for these models includes: ELM> MLP> Castagna et al. > Eskandari et al. > Pickett> Brocher. So, the result that shows the ELM model has higher accuracy than the other machine learning (MLP) approach and also other empirical equations (RMSE = 0.0444 km/s and R2 = 0.9809). Some advantages to the other artificial neural network approach include higher accuracy and performance characteristics, simple algorithm learning, improved performance, nonlinear conversion during training, no stuck in local optimal points, and it is over fitting. The novelty used in this paper is the type of newly implemented artificial model (ELM) and the number of input parameter. This approach possesses to the higher power, speed and accuracy than the methods used by other researchers to predict Vs.

Flow unit characterization plays an important role in heterogeneity analysis and reservoir simulation studies. Usually, a correct description of the lateral variations of reservoir is associated with uncertainties. From this point of view, the well data alone does not cover reservoir properties. Because of large well distances, it is difficult to build the model of a heterogenic reservoir, but 3D seismic data provides regular sampling that can improve reservoir spatial description.
In this study, seismic attribute analysis was used to predict flow zone indicator (FZI) values of a carbonate reservoir by using seismic and well log data. First, a 3D acoustic impedance volume was created as an external attribute for seismic data analysis. To improve the ability of FZI prediction, the maximum number of attributes from multiattribute analysis was computed by using a step-wise regression technique. To verify the results of multiattribute technique, the cross plot analysis of multiattribute method was performed. It was found that the R2 value of the correlation between the predicted and actual FZI is as high as 0.859 with an average error value of 2.34 µm. The analysis of the results of multiattribute technique showed that it was an effective technique for FZI prediction in hydrocarbon reservoirs. Such accuracy in building a 3D distribution of FZI provides a good insight into reservoir production zones. The results clearly indicate that the methodology proposed herein can successfully be used to specify the locations of new wells for the purpose of future production or injection plans.

Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced for both regression (support vector regression (SVR)) and classification (support vector classification (SVC)) problems. In the current study, to estimate the porosity of a carbonate reservoir in one of Iran south oil fields from well log data, the SVR model is firstly constructed; then the performance achieved is compared to that of an artificial neural network (ANN) model with a multilayer perceptron (MLP) architecture as a well-known method to account for the reliability of SVR or the possible improvement made by SVR over ANN models. The results of this study show that by considering correlation coefficient and some statistical errors the performance of the SVR model slightly improves the ANN porosity predictions.