Total organic carbon (TOC) content is one of the crucial parameters that determine the value of the source rock. The TOC content gives important indications about the source rocks and hydrocarbon volume. Various techniques have been utilized for TOC quantification, either by geochemical analysis of source rocks in laboratories or using well logs to develop mathematical correlations and advanced machine learning models. Laboratory methods require intense sampling intervals to have an accurate understanding of the reservoir, and depending on the thickness of the interested formation, it can be time-consuming and costly. Empirical correlations based on well logs (e.g., density, sonic, gamma ray, and resistivity) showed fast predictions and very reasonable accuracies. However, other important parameters such as thermal neutron logs have not been studied yet as a potential input for providing reliable TOC predictions. Also, different studies estimate the TOC based on the well-logging data for various formations; however, limited studies were reported to predict the TOC for the Horn River Formation. Therefore, the objective of this study is to estimate the TOC variations based on the thermal neutron logs using one of the largest source rocks in Canada: The Horn River Formation. More than 150 data sets were collected and used in this work. The parameters of the artificial neural network (ANN) model were fine-tuned in order to improve the model’s prediction performance. Furthermore, an empirical correlation was developed utilizing the optimized ANN model to allow fast and direct application for the developed model. The developed correlation can predict the TOC with an average absolute error of 0.52 wt %. The proposed TOC model was able to outperform the previous models, and the coefficient of determination was increased from 0.28 to 0.73. Overall, the proposed TOC model can provide high accuracy for TOC ranges from 0.3 to 6.44 wt %. The developed model can provide a real-time quantification for the organic matter maturity, helping to allocate the zones of mature organic matter within the drilled formations.
Summary Iron sulfide (FeS) scale is a known problem that can significantly impact oil and gas (O&G) production. However, current monitoring methods cannot detect the problem at early stages, not until it is too late for any meaningful remedial action. Spectral induced polarization (SIP) is an established geophysical method increasingly used in near-surface environmental applications. The unique characteristics of the SIP method, mainly the sensitivity to both bulk and interfacial properties of the medium, allow for the potential use as a characterization and monitoring tool. SIP is particularly sensitive to metallic targets, such as FeS, with direct implications for the detection, characterization, and quantification of FeS scale. In a column setup, various concentrations of pyrite (FeS2), a common form of FeS scale, within calcite were tested to examine the SIP sensitivity and establish qualitative and quantitative relationships between SIP signals and FeS2 properties. The concentration of FeS2 in the samples directly impacts the SIP signals; the higher the concentration, the higher the magnitude of SIP parameters. Specifically, the SIP method detected the FeS2 presence as low as 0.25% in the bulk volume of the tested sample. This study supports the potential use of SIP as a detection method of FeS2 presence. Furthermore, it paves the way for upcoming studies utilizing SIP as a reliable and robust FeS scale characterization and monitoring method.
Multiple challenges are associated with the characterization and development of unconventional shale reservoirs. The petrophysical properties play significant roles in hydrocarbon production from unconventional reservoirs. Several techniques can be used to determine the petrophysical properties such as routine core analysis, nuclear magnetic resonance (NMR), and dielectric techniques. This study presents an effective workflow to characterize the petrophysical properties of unconventional shales at different maturation stages. In this study, the conducted measurements are X-ray diffraction (XRD) analysis, Rock-Eval pyrolysis, helium porosity, NMR, and dielectric experiments. The rock samples were prepared for the measurements by drying the samples under a vacuum. In addition, the samples were artificially maturated using a muffle furnace at different temperatures and heating times. The impact of shale maturation on the petrophysical properties was captured by evaluating the rock properties after each maturation stage. Results show that the shale samples have a TOC of 17.5 wt.% on average, and a hydrogen index (HI) of 809, indicating that the samples are belonging to kerogen type I. The mineralogical analysis indicates that the used shale samples have a calcite percentage of around 59.9%. Moreover, the artificial maturation led to reducing the total organic content, due to the conversion of organic matter into hydrocarbon fluids. NMR and dielectric measurements showed that the shale porosity system was altered due to artificial maturation. The real dielectric constant was reduced indicating a reduction in the kerogen percentages. The cumulative probability density was increased after the maturation, revealing the shale porosity was increased which could be attributed to the dissolution of kerogen during the maturation process. Ultimately, this study improves our understanding of characterizing unconventional shale formations. Also, a reliable workflow is proposed for a better characterization of the unconventional formations by integrating routine core analysis, Rock-Eval, NMR, and dielectric techniques. Such workflow can pave the way to introduce a downhole technique to characterize unconventional resources more effectively.
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