Natural bee honey and Conservation of drone semen in liquid nitrogen

Purpose: Conducting a honey diluent test for creeples of sperm of a drone honey bee.Materials and methods. The material for the research served a sperm of the milled drone drums of the "Prioksky" type of the Midway breed of bees. The selection of sperm was carried out in June-July 2020 g by the method of artificial stimulation of the turning of the endofalosha in half-armed drones aged 25-30 days. The rock type "Prioksky" of the middle Russian breed of bees. Before freezing, the sperm was stored in glass capillaries in the cooled state at 3 ° C for 2 months. The following composition of the diluent was tested - 10% honey, lactose, sucrose, egg yolk and dimethyl sulfoxide.Results. Studies have shown the viability of sperm at 64.0 ± 1.8% (41.5-83.7), and a total mobility of 2.2 ± 0.6% (0-11.5). To evaluate the fertilizing ability of sperm, carried out artificial insemination of 10 bee modules. In 4 seeded bees dykens, the presence of sperm in a seed-hearter with a concentration of sperm from 0.22-4.4 million / μl is revealed. In paired eggs of three other seeded matters, the presence of sperm and the complete absence of spermatozoa in the seed-receptionist are recorded.Conclusion. Tests of the honey diluent for deep freezing sperm of the drone honey bees in liquid nitrogen confirmed its cryophylactic properties.

Rock Type Classification Models Interpretability Using Shapley Values

We propose a methodology to improve rock type classification using machine learning (ML) techniques and to reveal causal inferences between reservoir quality and well log measurements. Rock type classification is an essential step in accurate reservoir modeling and forecasting. Machine learning approaches allow to automate rock type classification based on different well logs and core data. In order to choose the best model which does not progradate uncertainty further into the workflow it is important to interpret machine learning results. Feature importance and feature selection methods are usually employed for that. We propose an extension to existing approaches - model agnostic sensitivity algorithm based on Shapley values. The paper describes a full workflow to rock type prediction using well log data: from data preparation, model building, feature selection to causal inference analysis. We made ML models that classify rock types using well logs (sonic, gamma, density, photoelectric and resistivity) from 21 wells as predictors and conduct a causal inference analysis between reservoir quality and well logs responses using Shapley values (a concept from a game theory). As a result of feature selection, we obtained predictors which are statistically significant and at the same time relevant in causal relation context. Macro F1-score of the best obtained models for both cases is 0.79 and 0.85 respectively. It was found that the ML models can infer domain knowledge, which allows us to confirm the adequacy of the built ML model for rock types prediction. Our insight was to recognize the need to properly account for the underlying causal structure between the features and rock types in order to derive meaningful and relevant predictors that carry a significant amount of information contributing to the final outcome. Also, we demonstrate the robustness of revealed patterns by applying the Shapley values methodology to a number of ML models and show consistency in order of the most important predictors. Our analysis shows that machine learning classifiers gaining high accuracy tend to mimic physical principles behind different logging tools, in particular: the longer the travel time of an acoustic wave the higher probability that media is represented by reservoir rock and vice versa. On the contrary lower values of natural radioactivity and density of rock highlight the presence of a reservoir. The article presents causal inference analysis of ML classification models using Shapley values on 2 real-world reservoirs. The rock class labels from core data are used to train a supervised machine learning algorithm to predict classes from well log response. The aim of supervised learning is to label a small portion of a dataset and allow the algorithm to automate the rest. Such data-driven analysis may optimize well logging, coring, and core analysis programs. This algorithm can be extended to any other reservoir to improve rock type prediction. The novelty of the paper is that such analysis reveals the nature of decisions made by the ML model and allows to apply truly robust and reliable petrophysics-consistent ML models for rock type classification.

Fracture Height Prediction Model Utilizing Openhole Logs, Mechanical Models, and Temperature Cooldown Analysis with Machine Learning Algorithms

Vertical wells require diagnostic techniques after minifrac pumping to interpret fracture height growth. This interpretation provides vital input to hydraulic fracturing redesign workflows. The temperature log is the most widely used technique to determine fracture height through cooldown analysis. A data science approach is proposed to leverage available measurements, automate the interpretation process, and enhance operational efficiency while keeping confidence in the fracturing design. Data from 55 wells were ingested to establish proof of concept.The selected geomechanical rock texture parameters were based on the fracturing theory of net-pressure-controlled height growth. Interpreted fracture height from input temperature cooldown analysis was merged with the structured dataset. The dataset was constructed at a high vertical depth of resolution of 0.5 to 1 ft. Openhole log data such as gamma-ray and bulk density helped to characterize the rock type, and calculated mechanical properties from acoustic logs such as in-situ stress and Young's modulus characterize the fracture geometry development. Moreover, injection rate, volume, and net pressure during the calibration treatment affect the fracture height growth. A machine learning (ML) workflow was applied to multiple openhole log parameters, which were integrated with minifrac calibration parameters along with the varying depth of the reservoir. The 55 wells datasets with a cumulative 120,000 rows were divided into training and testing with a ratio of 80:20. A comparative algorithm study was conducted on the test set with nine algorithms, and CatBoost showed the best results with an RMSE of 4.13 followed by Random Forest with 4.25. CatBoost models utilize both categorical and numerical data. Stress, gamma-ray, and bulk density parameters affected the fracture height analyzed from the post-fracturing temperature logs. Following successful implementation in the pilot phase, the model can be extended to horizontal wells to validate predictions from commercial simulators where stress calculations were unreliable or where stress did not entirely reflect changes in rock type. By coupling the geometry measurement technology with data analysis, a useful automated model was successfully developed to enhance operational efficiency without compromising any part of the workflow. The advanced algorithm can be used in any field where precise fracture placement of a hydraulic fracture contributes directly to production potential. Also, the model can play a critical role in cube development to optimize lateral landing and lateral density for exploration fields.

Laboratory Measurements Mapping Heterogeneity and Variation of Poroelasticity for Improved Hydraulic Fracture Design in a Tight Carbonate Reservoir Onshore UAE

Reservoir X is a thin and tight carbonate reservoir with thin caprock that isolates it from an adjacent giant reservoir. An accurate geomechanical model with high precision is required for designing the optimum hydraulic fracture and preventing communication with adjacent reservoirs. The reservoir exhibits considerable variability in rock properties that will affect fracture height growth, complexity, and width and rock interaction with treatment fluids. The heterogeneity observed from the tight sections is further complicated by the variation of Biot's poroelastic coefficient, α, which is required for accurate assessment of the effective stresses. Laboratory testing was required to characterize the extensive vertical heterogeneity for key inputs in developing a geomechanics model. Approximately 120 ft of continuous core from an onshore field was provided for this study. The core material represented a potential tight carbonate reservoir interval and bounding sections. Heterogeneity mapping was performed from continuous core measurements from CT-imaging and scratch testing. CT-imaging provides an indication of the bulk density variation and compositional changes. Scratch testing provides a continuous measure of the unconfined compressive strength (UCS). Combining the two provides a means for accurate definition of rock thickness for dense, moderately dense, and lower density material coupled with corresponding compressive strength. Rock units were then subdivided based on these continuous properties for further geomechanics tests. Using log analysis combined with continuous UCS measurements from scratch testing, eight rock type classes were defined covering the target reservoir interval and bounding sections. This information was used for optimizing the sample selection process to characterize each identified rock unit. Routine core analysis measurements reveal significant vertical heterogeneity with porosity ranging from 0.1% to 18.1%. Similar variability was determined from elastic properties for each of the eight rock types. Quasi-static values for Young's modulus and Poisson's ratio determined at in-situ stress conditions ranged from 2.6 to 9.6 × 106 psi, and from 0.16 to 0.34, respectively. The Biot's poroelastic coefficient has a first-order impact on the calculated effective stress profile, which directly affects fracture stimulation model results. Testing from this study combined with previous measurements (Noufal et al. 2020, SPE-202866-MS) provides a unique correlation with porosity and bulk compressibility. In addition, rock-fluid compatibility was evaluated with proppant embedment/fracture conductivity tests. Results are dependent on a given rock type, exhibiting a wide range of fracture conductivity as a function of closure stress from 10 to 1000 md-ft. Embedment for all cases was low to moderate.

Innovative High Permeability Streak Characterization and Modeling Utilizing Static and Dynamic Data in a Complex Giant Mature Oil Field in the Middle East

This paper proposes an integrated approach to model High Permeability Streaks (HPS) using the case study of heterogeneous carbonate Reservoir B, utilizing static and dynamic data. Modelling the HPS is critical as they play an important role in fluid dynamics within the reservoir. The impact is observed from 60 years of development, where flood front movement is captured by rich density of Pulsed Neutron and recently drilled open hole logs. Injection water is overriding from tighter lower subzones (injected zones) to permeable upper subzones of the reservoir, thereby leaving the tighter lower subzones unswept. Gas cusping down to the oil zone occurs through the HPS resulting in non-uniform gas cap expansion, which leads to early gas breakthrough in producers near the gas cap. The problem with characterizing HPS is associated with their thickness- in Reservoir B it ranges from 0.5 to 2.5ft and occur in multiple subzones in the upper part of the reservoir. The standard triple combo suite of logs does not have the resolution to detect these thin HPS. In addition, the cored interval of the HPS is mainly disintegrated which is attributed majorly to well sorted grain-supported lithofacies. Therefore, sampling for porosity & permeability via Routine Core Analysis (RCA) and Capillary pressure as well as pore throat distribution using Mercury Injection Capillary Pressure (MICP) method is extremely difficult. This results in a gap in the input dataset for the static models, where the higher permeability samples are not captured in logs or cores and are therefore under-represented. Current approach to unify this gap is to use permeability multipliers, which does not honor geological trends. The HPS in Reservoir B has added complexities when compared to other regional HPS. Not only are they multiple and distributed across subzones, there is also preferential movement of water through the HPS within the same area. Of the 3 upper subzones that have HPS, in some areas, water injected in lower subzone will override the HPS in the middle and move right to the HPS in the top subzone, thereby ignoring the hierarchical flood front movement from bottom to the top. A robust workflow was developed in order to address and resolve the above mentioned uncertainties related to High Permeability Streaks. The proposed integrated workflow consisted of five stages: Developing a robust geological conceptual model Mapping spatial distribution & continuity Capturing the vertical presence in cored & uncored wells (depth & thickness) Permeability Quantification of HPS using Well Test Measurements Modelling High Permeability Streaks The paper highlights the utilization of a range of static (core, Routine Core Analysis (RCA), image logs, OH logs) and dynamic data (Pulse Neutron Logs (PNL's), later drilled Open Hole Logs, Production Logging Tools (PLTs) and well test data). Quantitative (HPS depth indicated by water saturation profile indicated by waterflood movement) and Qualitative (Flooding observed but HPS depth is uncertain) depth indicators/flags were generated from the data set and became the foundation of the modelling the HPS. The first step in the workflow is to establish a robust geological conceptual model. For Reservoir B, certain facies contribute to HPS, which are mainly leached Rudist Rudstones and Coated grain Algal Floatstones as well as well sorted Skeletal Grainstones. Based on core observations, they have confirmed vertical stratigraphic presence in each subzone (top, mid, base) which is attributed to storm events. These were consequently mapped using average thickness from core descriptions and revised using contributing facies trend maps and qualitative dynamic observations. These maps served as basis for probability trend distribution for static rock type models. The vertical presence of HPS was increased from 10% to 30% by re-introducing them in the missing core intervals using quantitative dynamic flags and thickness from isochores. Consequently, permeability were assigned in the missing section using the proposed permeability enhancement technique that honors the verified well test measurements. Based on the above improvements, the HPS intervals were mapped to the static rock type with best reservoir quality (SRT 1), which is also linked to certain geological attributes (i.e. lithofacies, diagenetic overprint & depositional environment). The enhanced permeability in the identified HPS intervals is also reflected as upgraded SRT (from lower SRT 2 to best SRT 1). The overall impact is observed by improvement of poro-perm cloud, with added control points for HPS SRT (1), which is vital for permeability modelling. The updated permeability model, captures high perm streaks in terms of vertical presence and magnitude. By introducing higher permeability in the upper subzones of the reservoir, the water overriding/gas cusping phenomena could then be mimicked in the dynamic model. The proposed methodology is an integrated workflow that maximizes the input from each disciplines (G&G, Petrophysics and Reservoir Engineering) to create a robust static model through incorporation of high permeability streaks. The use static and dynamic data, has helped to establish HPS existence/preference, which then could be used to upgrade the permeability/SRT. This will in turn lead to a better static model and a better history match in the dynamic model. It will also led to better remaining in place prediction and enable accurate prediction for future field development, especially where EOR is involved.

The Application of Computed Tomography Scanning and Nuclear Magnetic Resonance for Rock Typing of Polymineral Clastic Reservoirs

Summary The main objective of this study was to provide rock typing of the producing formation based on high-resolution computed tomography (CT) scanning and nuclear magnetic resonance (NMR) data in combination with routine core analyses results. The target formation is composed of a shallowing up sequence of clastic rocks. Siltstones in its base are gradually replaced by sandstones toward its top. Initially, only sandstones were considered as oil-bearing, while siltstones were considered as water-bearing based on saturation calculation by means of Archie’s equation (Archie 1942) with the same values of cementation and saturation exponent for the whole formation. However, follow-up well tests detected considerable oil inflow also from the base of the reservoir composed of siltstones. Therefore, better rock typing was needed to improve the initial saturation distribution calculation. An applied approach that was based on integrated analysis of rock microstructural characteristics and derived from the NMR and CT techniques and conventional properties used for reserves calculation appeared to be an effective tool for rock typing polymineral clastic reservoirs. Measuring porous network characteristics and conventional properties in the same core plug enables a confident correlation between all measured parameters. Consequently, rock typing of samples based on flow units’ microstructural characteristics derived from NMR and CT scanning has shown a very good consistency with each other. As a result, four rock types were distinguished within a formation, which were previously interpreted as a single rock type. The detailed rock typing of the reservoir allowed more accurate reserves calculation and involvement of additional intervals into the production. Besides porous media characterization, CT scanning proved to be an effective tool for detecting minerals, such as pyrite and carbonates, characterizing depositional environments. Increasing content of pyrite in siltstones, detected by CT scanning and X-ray fluorescence spectroscopy, indicates deeper and less oxic conditions, while the presence of carbonate shell debris indicates shallower, more oxic depositional settings. The NMR test results show that the NMR signal distribution is affected by both pore size distribution and mineralogical composition. An increase of pyrite content caused shifting of the T2 distribution to the lower values, while carbonate inclusions caused shifting of the T2 distribution to higher values relative to the other samples not affected by these mineral inclusions. Because NMR distribution is affected by multiple factors, applying Т2cutoff values alone for rock typing can lead to ambiguous interpretation. Applying CT scanning next to NMR data increases the reliability of rock typing. The proposed laboratory workflow, including a combination of nonhazardous and nondestructive tests, allowed reliable differentiation of the rock samples based on multiple parameters that were interpreted in relationship with each other. Because the designed laboratory test workflow enabled both justified separation of the samples by rock type and determination of parameters used for reserves calculation, it can be recommended for further application in polymineral clastic reservoirs. Because the proposed techniques are nondestructive, the same samples can be applied for multiple tests including special core analysis (or SCAL).

LANDSLIDE SUSCEPTIBILITY MAPPING IN BERASTAGI DISTRICT KARO REGENCY BASED ON GEOGRAPHIC INFORMATION SYSTEMS

This study aims to describe the spatial distribution of landslide prone areas in Berastagi District, Karo Regency with a Geographic Information System approach. Determination of the level of landslide susceptibility is obtained based on the method of scoring and weighting and overlaying of parameters including slope, rainfall, land use, soil type, rock type, and landform. The results showed that there were 4 levels of landslide susceptibility in Berastagi District, Karo Regency, namely low level of susceptibility 1,036.76 Ha (33.16%), medium level of susceptibility 772.02 Ha (24.69%), high level of susceptibility 1,055 .53 Ha (33.76 %), and very high level of susceptibility 262.13 Ha (8.38 %). The low level of susceptibility is dominated by Guru Singa Village (10.83 %), Raya Village (8.66 %), and Rumah Berastagi Village (3.72%). The medium and high level of susceptibility were dominated by Sempajaya Village at 4.51% and 3.68%. The very high level of susceptibility to landslides is dominated by Doulu Village, which is 17.40%. Meanwhile, the other 5 (five) villages have varying levels of susceptibility to landslides. Thus, 33.16% of the Berastagi District area is still safe from landslides, while the remaining 66.83% is prone to landslides.

A Study on Petrographic Characteristics and Provenance for the Rock Properties from Dorim-ri Tombs of Baekje Kingdom in Cheonan, Korea

We analyzed the provenance and petrographic characteristics for the rock properties from stone-lined tomb and stone chamber tomb at the Dorim-ri site of the Baekje Kingdom, located in Cheonan. The two tombs consist of 10 kinds of rocks including gneiss, diorite, and andesite. The major rock type is gneiss (54.3%), which composes the main chamber walls of the tombs. Diorite (11.3%) and andesite (10.6%) also make up a large percentage of the rocks, tending to be used to fill the space between the main chamber walls. Thus, the stones appear to have been used according to their shape and the disposition of the site, respectively. Investigation of their provenance, confirmed their source area to be near the Ipjang Reservoir, about 1 km away from the site, and their procurement was probably conducted via a waterway. This result might serve as basic data regarding the material procurement system of ancient tomb culture and for preservation measures for archaeological sites.