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This hand guide in the Gulf Drilling Guides series offers practical techniques that are valuable to petrophysicists and engineers in their day-to-day jobs. Based on the authors many years of experience working in oil companies around the world, this guide is a comprehensive collection of techniques and rules of thumb that work.The primary functions of the drilling or petroleum engineer are to ensure that the right operational decisions are made during the course of drilling and testing a well, from data gathering, completion and testing, and thereafter to provide the necessary parameters to enable an accurate static and dynamic model of the reservoir to be constructed. This guide supplies these, and many other, answers to their everyday problems.

In hydrocarbon reserves, porosity is an important parameter that defines the volume and mobility of the porous fluid. Reservoir and management operations are greatly influenced by porosity. Usually, the standard methods for determining porosity are core analysis and well testing. These methods are very expensive, and generally wells in a field do not have a core. As a result, the methods that can determine the petrophysical properties of the reservoir, including porosity and well logging charts, are very important because well logs are usually available for all wells of a field. Artificial intelligence methods are new, low-cost and accurate methods that can indirectly estimate reservoir porosity in the shortest possible time using well-logging data. In this study, a new intelligent method of support vector regression with sparrow search algorithm (SVR-SSA) was used to indirectly estimate the porosity of a hydrocarbon reservoir in southwestern Iran (Azadegan oil field). Then, the performance of the hybrid model was compared to that of support vector regression (SVR). A total of 2506 well logging data were included in the database and were divided into two categories of training data (1754 data points) and test data (752 data points) for evaluating models. For the training data set of the SVR-SSA model, R2, mean squared error (MSE), and root mean squared error (RMSE) values were 0.98, 0.000933, and 0.030555, and those for the SVR model were 0.9072, 0.001096 and 0.033108, respectively. Also, for the SVR-SSA model test data set, R2, MSE, and RMSE values were 0.9726, 0.001032, and 0.032128 and those for the SVR model were 0.8931, 0.001660 and 0.040750, respectively. Comparing SVR-SSA and SVR based on R2, MSE and RMSE performance indicators revealed that SVR-SSA outperformed other models in predicting porosity. SVR-SSA is, therefore, a powerful, fast and accurate method of indirectly estimating porosity in reservoirs where porosity is not measured directly in the core.

Today, researchers face multiple challenges identifying clay mineral types and lithofacies from well log data. This research paper hopes to offer new insight into this particular challenge. Formation evaluation characteristics play a significant role in the exploration and production of future and current oil and gas fields. The proposed methodology in this study uses an integrated approach that includes: (1) numerical equations, (2) Neuro-Fuzzy neural networks, (3) cross plots, and (4) statistical analyses. This proposed integrated approach is capable of dramatically improving the accuracy of the results. Well logging data provide valuable information for identifying lithofacies, clay mineralogy types, as well as other important hydrocarbon reservoir characteristics. Talhar Shale in the Southern Lower Indus Basin, Pakistan, is composed of interbedded shale, sand, and shaly-sand, intervals that have been identified via the lithological interpretation process of well logs. Talhar Shale contains montmorillonite type clay with minor amounts of illite, glauconite, and various micas that can be easily identified by natural gamma ray absorption profiles, as well as through ratio logs, bulk density log, and photoelectric absorption index log. These interpretations can be further confirmed via cross plots and other statistical analyses. This approach consists of a comprehensive study of well logging data and thus can lend itself to be a helpful component in characterizing the hydrocarbon structures of the Talhar Shale.

The Muli area has dense lithology, high hardness, poor porosity and permeability, and well-developed micro-fractures, which is different from other gas hydrate regions with unconsolidated sediment characteristics. With the gradual deepening of gas hydrate scientific research in the Muli area, some unique low-resistivity gas hydrate phenomena have been discovered. The understanding of the genesis mechanism of low-resistivity gas hydrate is not clear, which makes it difficult to identify gas hydrate layers and adversely affects the evaluation of reservoir parameters and reserves prediction. In this investigation, the well logging response law of pore-filling and fracture-filling gas hydrate reservoirs is systematically combined and the associated reservoir characteristics relationships are evaluated as a function of lithology, physical properties, reservoir space, gas hydrate and fluid distribution characteristics. Then, the correlation between cementation index and pore structure complexity index was fitted. Finally, a new gas hydrate saturation calculation model is proposed. The results show that the new saturation calculation model is in good agreement with the well logging interpretation of gas hydrate, which can identify low-resistivity gas hydrate effectively. The results of this study thus assist in terms of reliable gas hydrate exploration in the Muli area.

Well logging curves serve as indicators of strata attribute changes and are frequently utilized for stratigraphic analysis and comparison. Deep learning, known for its robust feature extraction capabilities, has seen continuous adoption by scholars in the realm of well logging stratigraphic correlation tasks. Nonetheless, current deep learning algorithms often struggle to accurately capture feature changes occurring at layer boundaries within the curves. Moreover, when faced with data imbalance issues, neural networks encounter challenges in accurately modeling the one-hot encoded curve stratification positions, resulting in significant deviations between predicted and actual stratification positions. Addressing these challenges, this study proposes a novel well logging curve stratigraphic comparison algorithm based on uniformly distributed soft labels. In the training phase, a label smoothing loss function is introduced to comprehensively account for the substantial loss stemming from data imbalance and to consider the similarity between different layer data. Concurrently, spatial attention and channel attention mechanisms are incorporated into the shallow and deep encoder stages of U 2 -Net, respectively, to better focus on changes in stratification positions. During the prediction phase, an optimized confidence threshold algorithm is proposed to constrain stratification results and solve the problem of reduced prediction accuracy because of occasional layer repetition. The proposed method is applied to real-world well logging data in oil fields. Quantitative evaluation results demonstrate that within error ranges of 1, 2, and 3 m, the accuracy of well logging curve stratigraphic division reaches 87.27%, 92.68%, and 95.08%, respectively, thus validating the effectiveness of the algorithm presented in this paper.

Lake Towuti is located on central Sulawesi/Indonesia, within the Indo Pacific Warm Pool, a globally important region for atmospheric heat and moisture budgets. In 2015 the Towuti Drilling Project recovered more than 1000 m of drill core from the lake, along with downhole geophysical logging data from two drilling sites. The cores constitute the longest continuous lacustrine sediment succession from the Indo Pacific Warm Pool. We combined lithological descriptions with borehole logging data and used multivariate statistics to better understand the cyclic sequence, paleoenvironments, and geochronology of these sediments. Accurate chronologies are crucial to analyze and interpret paleoclimate records. Astronomical tuning can help build age-depth models and fill gaps between age control points. Cyclostratigraphic investigations were conducted on a downhole magnetic susceptibility log from the lacustrine facies (10–98 m below lake floor) from a continuous record of sediments in Lake Towuti. This study provides insights into the sedimentary history of the basin between radiometric ages derived from dating a tephra layer (~ 797 ka) and C14-ages (~ 45 ka) in the cores. We derived an age model that spans from late marine isotope stage (MIS) 23 to late MIS 6 (903 ± 11 to 131 ± 67 ka). Although uncertainties caused by the relatively short record and the small differences in the physical properties of sediments limited the efficacy of our approach, we suggest that eccentricity cycles and/or global glacial-interglacial climate variability were the main drivers of local variations in hydroclimate in central Indonesia. We generated the first nearly complete age-depth model for the lacustrine facies of Lake Towuti and examined the potential of geophysical downhole logging for time estimation and lithological description. Future lake drilling projects will benefit from this approach, since logging data are available just after the drilling campaign, whereas core descriptions, though more resolved, only become available months to years later.

Neutron well logging, using instruments equipped with neutron source and detectors (e.g., 3He-tubes, NaI, BGO), plays a key role in lithological differentiation, porosity determination, and fluid property evaluation in the petroleum industry. The growing trend of multifunctional neutron well logging, which enables simultaneous extraction of multiple reservoir characteristics, requiring high-performance detectors capable of withstanding high-temperature downhole conditions, limited space, and instrument vibrations, while also detecting multiple particle types. The Cs2LiYCl6:Ce3+ (CLYC) elpasolite scintillator demonstrates excellent temperature resistance and detection efficiency, making it become a promising candidate for leading the development of the novel neutron-based double-particle logging technology. This study employed Monte Carlo simulations to generate equivalent gamma spectra and proposed a pulse shape discrimination simulation method based on theoretical analysis and probabilistic iteration. The performance of CLYC was compared to that of common detectors in terms of physical properties and detection efficiency. A double-particle pulsed neutron detection system for porosity determination was established, based on the count ratio of equivalent gamma rays from the range of 2.95–3.42 MeVee energy bins. Results showed that CLYC can effectively replace 3He-tubes for porosity measurement, providing consistent responses. This study offers numerical simulation support for the design of future neutron well logging tools and the application of double-particle detectors in logging systems.

To help understand volcanic facies in the subsurface, data sets that enable detailed comparisons between down-hole geophysical data and cored volcanic intervals are critical. However, in many cases, the collection of extended core intervals within volcanic sequences is rare and often incomplete due to challenging coring conditions. In this contribution we outline and provide initial results from borehole logging operations within two fully cored lava-dominated borehole sequences, PTA2 and KMA1, on the Big Island of Hawai`i. Data for spectral gamma, magnetic susceptibility, dipmeter resistivity, sonic, total magnetic field, temperature and televiewer wireline logs were successfully acquired for the open hole interval ca. 889 m to 1567 m within the PTA2 borehole. Spectral gamma was also collected from inside the casing of both wells, extending the coverage for PTA2 to the surface and covering the interval from ca. 300 to 1200 m for KMA1. High-quality core material was available for both boreholes with almost complete recovery which enabled high-resolution core-to-log integration. Gamma data are generally low commonly in the range ca. 7–20 gAPI but are shown to increase up to API of ca. 60 with some intrusions and with increases in hawaiite compositions in the upper part of PTA2. Velocity data are more variable due to alteration within porous volcanic facies than with burial depth, with a general degrease down-hole. The high-resolution televiewer data have been compared directly to the core, enabling a comprehensive analysis of the variations in the televiewer responses. This has enabled the identification of key features including individual vesicles, vesicle segregations, strained vesicles, chilled margins, rubble zones, intrusive contacts and pāhoehoe lobe morphologies, which can be confidently matched between the televiewer data and the full diameter core. The data set and results of this study include findings which should enable improved borehole facies analysis through volcanic sequences in the future, especially where down-borehole data and images but no core are available.

This study investigates the geological and geophysical exploration of quartzite, gneiss, schist, and meta-sedimentary rocks in the Kurumalai region of southern India, providing new insights into the variety of geological area. The Kurumalai hills, located among significant features such as PandraMalai, Manthithoopu, and Oodumalai, reveal a diverse range of geological formations, including fissures, weathered rock, breccia silt, and gravel containing quartzite, as well as complicated gneiss formation. Notably, our examination indicates severely worn and red-oxidized quartzite with a major trend of N5°W-S5°W and a dip of 65°W under microscopic observation. Texture study reveals the thin and coarse-grained structure of the rocks, which have a subhedral mosaic texture, showing the presence of microscopic quartz grains enclosed in embedded corner grains. This study uses 2D Electrical Resistivity Tomography (ERT) to determine the geological structure and slope of the Kurumalai hills, properly identifying quartzite, granite, sandstone, and calcareous intrusions. Our method combines the 2D ERT and magneto telluric (MT) methodologies and correlates them with borehole logging data from the Central Groundwater Board (CGWB) and the Indian government. This study contributes to a better understanding of the region geological landscape by shedding light on the geotechnical relevance of Archean quartzite and rock formations in the studied area. Our findings provide useful insights that can be used to inform future geological investigations and better understand the geological development of the Kurumalai region.