Water saturation
assessment is recognized as one of the most critical
aspects of formation evaluation, reserve estimation, and prediction
of the production performance of any hydrocarbon reservoir. Water
saturation measurement in a core laboratory is a time-consuming and
expensive task. Many scientists have attempted to estimate water saturation
accurately using well-logging data, which provides a continuous record
without information loss. As a result, numerous models have been developed
to relate reservoir characteristics with water saturation. By expanding
the use and advancement of soft computing approaches in engineering
challenges, petroleum engineers applied them to estimate the petrophysical
parameters of the reservoir. In this paper, two techniques are developed
to estimate the water saturation in terms of porosity, permeability,
and formation resistivity index through the use of 383 data sets obtained
from carbonate core samples. These techniques are the nonlinear multiple
regression (NLMR) technique and the artificial neural network (ANN)
technique. The proposed ANN model achieved outstanding performance
and better accuracy for calculating the water saturation than the
empirical correlation using NLMR and Archie equation with a high coefficient
of determination (R
2) of 0.99, a low average
relative error of 1.92, a low average absolute relative error of 13.62,
and a low root mean square error of 0.066. To the best of our knowledge,
the current research establishes a novel foundation using the ANN
model in the estimation of water saturation.
Successful drilling operations require optimum well planning to overcome the challenges associated with geological and environmental constraints. One of the main well design programs is the mud program, which plays a crucial role in each drilling operation. Researchers focus on modeling the rheological properties of the drilling fluid seeking for accurate and real-time predictions that confirm its crucial potential as a research point. However, only substantial studies have real impact on the literature. Several AI-based models have been proposed for estimating mud rheological properties. However, most of them suffer from nonbeing field applicable attractive due to using non-readily field parameters as input variables. Some other studies have not provided a comprehensive description of the model to replicate or reproduce results using other datasets. In this study, two novel robust artificial neural network (ANN) models for estimating invert emulsion mud plastic viscosity and yield point have been developed using actual field data based on 407 datasets. These datasets include mud plastic viscosity (PV), yield point (YP), mud temperature (T), marsh funnel viscosity (MF), and solid content. The mathematical base of each model has been provided to provide a clear means for models' replicability. Results of the evaluation criteria depicted the outstanding performance and consistency of the proposed models over extant ANN models and empirical correlations. Statistical evaluation revealed that the plastic viscosity ANN model has a coefficient of determination (R 2 ) of 98.82%, a root-mean-square error (RMSE) of 1.37, an average relative error (ARE) of 0.12, and an absolute average relative error of 2.69, while for yield point, this model has a coefficient of determination (R 2 ) of 94%, a root-mean-square error (RMSE) of 0.76, an average relative error (ARE) of −0.67, and an absolute average relative error of 3.18.
Mossbauer spectroscopy has been used for the study of Egyptian goethite from different deposits of the Bahariya Oasis, in order to identify the different iron forms in the ore, and to investigate the physical properties of the mineral and its changes according to the geochemical conditions under which the ore was formed. Four representative samples were selected: the first one showed the existence of haematite with goethite, which is the normal final form to which goethite is transformed under high temperatures; the second gave two internal magnetic fields which could be due to water excess; the third one showed a relaxation effect due to small particle size; the fourth gave an extra hyperfine pattern with negative isomer shift which is still under investigation. The results obtained were confirmed by the x-ray diffraction and thermal analysis. A synthetic sample of e-FeOOH has been measured for comparison, in the temperature range from 77 to 392 K (the Nee1 point).
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