Reserve Estimation Research Articles

Material balance analysis of water influx into an undersaturated oil reservoir using pot aquifer model: a case study

ABSTRACT Early water breakthrough due to pressure depletion is often attributed to a strong water drive and must be established to avoid wrong reserves estimation and planning. In this study, we evaluated the Safsaf C reservoir, an undersaturated oil reservoir in North Africa that experienced early water breakthrough at 62 days after the commencement of oil production. To establish or disprove the presumption of it producing under a strong water drive, we critically analyzed the reservoir using the Material Balance Equation (MBE) coupled with the pot aquifer water influx model. The Campbell plot showed a weak aquifer, and the Solution plot confirmed aquifer presence. The pot aquifer plot gave the original aquifer in place an estimate of about 28.42 mmRB, which is about half the size of the estimated hydrocarbon pore volume of 51.15 mmRB. Water influx plots produced improved Oil initially in place (OIIP) estimates in the range of 11.50 to 12.20 mmSTB. The aquifer influx profiles show that water encroaches into the reservoir at a rate of 742 rbl/d. This approach is suitable when little is known about the aquifer and provides reliable estimates of OIIP and the degree of aquifer support for reservoirs bounded by small aquifers.

Analyzing the wettability changes of rocks in various states of core plugs

Current core plugs preparation standards for laboratory studies are developed for water-wet reservoirs and do not consider other types of wettability. Researching how different core preparation methods affect laboratory studies and wettability changes is relevant and important for petrophysical reserve estimation. Preconditions of doing this work was the question how core plugs impacts wettability changes in carbonate formations from several Eastern Siberian fields. The article describes the SCAL results of measuring the USBM wettability on core plugs at various states: a core plug with preserved saturation (before extraction), a core plug after extraction, and a core plug after wettability restoration. The authors showed how each of the stages of core plugs preparation affects wettability changes in rocks, the study of which is complicated by strong diagenetic changes: uneven salinization, bituminization, and anhydritization. The most accurate results were obtained from core plugs with preserved saturation. Overall, wettability of core plugs shifted towards a more hydrophilic state after extraction, but extraction did not fully change wettability from hydrophobic to hydrophilic.

Features of laboratory study of the core of weakly cemented reservoir rocks

Background. Currently, there is a problem of studying intervals represented by weakly cemented core, although such rocks can be the main reservoirs. Lack of reliable petrophysical information leads to underestimation of the main parameters of porosity and permeability, which in the future is likely to lead to incorrect estimation of hydrocarbon reserves. The relevance of the work is due to the lack of unified regulatory and methodological approaches to the research of weakly cemented core material. Objective. To develop our own methodological approach by selecting technologies and techniques, which allows to carry out primary preparation, sampling, profile measurements and petrophysical studies of weakly cemented core. Materials and methods. We used weakly cemented core from the Volga-Ural oil and gas province and conducted the research in accordance with the requirements specified in the guidelines for laboratory tests using necessary supplies. The methods considered in the article cover the time interval from the early 2000s. Results. In the course of the work, standard methods of core investigation were considered, and the possibility of their application to the samples of weakly cemented rock was evaluated. The peculiarities of sample preparation and research on weakly cemented core were also revealed. Conclusions. A methodological approach was developed that allows primary preparation, sampling, profile measurements and petrophysical studies of weakly cemented core.

Geochemical characterization and reserve estimation of the economic heavy minerals of Sonadia Island, Bangladesh

The goal of the current study is to understand the elemental concentration, reserve assessment, mineralogical composition, and distribution of heavy minerals in the dune sands, foreshore, and backshore of Sonadia Island, Southeast Bangladesh. The study area yielded a number of drill hole composite sand samples that were used for density and magnetic separation of heavy minerals. These samples were then subjected to petrographic characterization, grain counting, mineralogical analyses using scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM–EDX), x-ray diffraction (XRD), and bulk sand geochemistry using X-ray fluorescence (XRF). The results of heavy liquid separation show that the total heavy mineral (THM) enrichments in foreshore sand (about 4.86%), backshore sand (about 9.03%), and dune sand (about 22.28%) are significantly higher. After characterizing the sand samples from Sonadia Island based on the abundance of each mineral species, the average content values relative to total economic heavy minerals (TEHM) were determined to be 38.14% ilmenite, 5.74% magnetite, 51.52% garnet, 1.01% zircon, 3.57% rutile, and 0.01% monazite. An SEM–EDX examination reveals that the elemental concentration of ZrO2 in zircon is 66.60%, while the average TiO2 content in rutile is ~ 95%, whereas TiO2 is 45% and the total Fe2O3 is 45.70% in ilmenite. Major phases including amphibole (tremolite) 45.9%, muscovite 15.6%, quartz 12.6%, garnet (almandine) 10.9%, dolomite 7.9%, ilmenite 4.1%, and kaolinite 1.3% are also identified by the XRD pattern. There are an estimated 19,794 tons of garnet in the foreshore sand, 47,324 tons in the backshore sand, and 276,868 tons in the dune sand within the reserve, making it the most prevalent heavy mineral. Ilmenite is the second most common heavy mineral, with quantities detected in the foreshore (15,016 tons), backshore (30,487 tons), and dune sand (215,092 tons).

World-class amethyst-agate geodes from Los Catalanes, Northern Uruguay: genetic implications from fluid inclusions and stable isotopes

AbstractThe amethyst and agate geodes from the Los Catalanes Gemmological District in Uruguay represent one of the main deposits of its kind worldwide. The geometry of the deposit is horizontal, with an irregular distribution of amethyst geodes within the upper level of the basalt lava flows and shows strong variations in their abundance, as well as quality, geometry, and shape. Reliable exploration guides are scarce, and the limited knowledge of the geological parameters controlling its occurrence makes exploration unpredictable, leading to inaccurate reserve estimation. Based on cutting-edge methods including nucleation-assisted microthermometry of one-phase fluid inclusions and determination of triple oxygen isotope in silicates and carbonates, as well as analysis of geode-hosted water and groundwater, we estimate the crystallisation temperatures in the range between 15 and 60 °C. These low temperatures point to amethyst crystallisation after the emplacement of the complete basalt pile. The mineralising fluid shows isotopic signatures consistent with meteoric water and very low salinities from pure water up to rarely over 3 wt% NaCl-eq., likely sourced from the groundwater hosted in the aquifers in the basaltic sequence and underlying units. Based on the insights provided by the new data, we propose the combination of open- and closed-system crystallisation inside pre-existing cavities due to the episodic infiltration of meteoric water in a rather stable geological context.

A Quantitative Particle-Based Approach for the Geometallurgical Assessment of Tailings Deposits

Tailings generated during ore processing may host significant residual contents of valuable commodities, including critical metals. The particle properties of the tailings, such as mineralogy, particle size, and the surface liberation of ore minerals, strongly control processing behaviour. This study explores a novel combination of methods for incorporating particle data, derived from automated mineralogy, into geometallurgical models of tailings deposits to better understand their reprocessing potential and the economic feasibility of re-mining. This was achieved through binning of different particle types, geostatistical modelling of particle bin frequencies, and bootstrap resampling to reconstruct particle populations. The spatial distributions of processing-relevant particle properties throughout the tailings deposit were predicted with corresponding uncertainties. There are clear systematic trends in the spatial distributions of different particle types, resulting from the sedimentary-style deposition of the tailings. For instance, the tailings nearer the dam walls comprise coarser, silicate-rich particles, while fine-grained and well-liberated sulphide mineral particles are more abundant in the centre of the tailings deposit. As a result, robust models could be developed for the spatial distributions of particle size and mineralogy, which strongly control the sorting of particles during deposition, and other related properties, such as sulphide mineral grain sizes. Finally, a bulk sulphide flotation process was simulated and acid mine drainage potential estimated using the interpolated particle data. Around 58% of the sulphide minerals present could be recoverable by flotation, with the recoverable sulphide portion decreasing towards the centre of the TSF due to the fine-grained nature of the sulphide minerals. The acid mine drainage potential of the tailings is estimated to be moderate to high, indicating that the carbonate minerals present are not sufficient to neutralise the high acid-generating potential of the sulphide minerals. Overall, this study demonstrates how particle-based geometallurgical models can be developed and utilised for practical applications, with the aim of improving the accuracy of resource and reserve estimations of tailings deposits and the sustainable and responsible management of anthropogenic resources. The methodology proposed here can be easily transferred to other tailings deposits.

Decoupling and predicting natural gas deviation factor using machine learning methods

Accurately predicting the deviation factor (Z-factor) of natural gas is crucial for the estimation of natural gas reserves, evaluation of gas reservoir recovery, and assessment of natural gas transport in pipelines. Traditional machine learning algorithms, such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory Neural Networks (BiLSTM), often lack accuracy and robustness in various situations due to their inability to generalize across different gas components and temperature-pressure conditions. To address this limitation, we propose a novel and efficient machine learning framework for predicting natural gas Z-factor. Our approach first utilizes a signal decomposition algorithm like Variational Mode Decomposition (VMD), Empirical Fourier Decomposition (EFD) and Ensemble Empirical Mode Decomposition (EEMD) to decouple the Z-factor into multiple components. Subsequently, traditional machine learning algorithms is employed to predict each decomposed Z-factor component, where combination of SVM and VMD achieved the best performance. Decoupling the Z-factors firstly and then predicting the decoupled components can significantly improve prediction accuracy of all traditional machine learning algorithms. We thoroughly evaluate the impact of the decoupling method and the number of decomposed components on the model’s performance. Compared to traditional machine learning models without decomposition, our framework achieves an average correlation coefficient exceeding 0.99 and an average mean absolute percentage error below 0.83% on 10 datasets with different natural gas components, high temperatures, and pressures. These results indicate that hybrid model effectively learns the patterns of Z-factor variations and can be applied to the prediction of natural gas Z-factors under various conditions. This study significantly advances methodologies for predicting natural gas properties, offering a unified and robust solution for precise estimations, thereby benefiting the natural gas industry in resource estimation and reservoir management.

A hydraulic-mechanical coupled triple porosity fractal model considering action mechanisms and gas transport mechanisms in carbon-hydrogen reservoirs

The shale formation, a common carbon-hydrogen reservoir, has numerous nanoscale pores that facilitate multiple gas transport mechanisms, including viscous flow, slip flow, Knudsen diffusion, and surface diffusion. Additionally, various action mechanisms involving effective stress, real gas effect, gas adsorption and desorption, and fracture-pore characteristics significantly influence gas transport. In this study, we conceptualize shale as a triple porosity medium comprising organic matrix, inorganic matrix, and microfractures. For each system, its hydraulic-mechanical coupled model with action mechanisms and gas transport mechanisms is derived based on fractal theory. After validating the model with Barnett and Marcellus data, the evolution of apparent permeability and gas pressure is studied, as well as the influences of pore fractal features, the real gas effect, and the number of hydraulic fractures on gas extraction. Simulation results indicate that, in comparison to organic matrix, inorganic pore fractal features exert a more noticeable effect on cumulative production. The real gas effect is critical in gas transport and reservoir reserve estimation; disregarding this effect results in a 17.4% underestimation of cumulative gas production by the 1600th day. Fracture interference attenuates the improvement in cumulative production gains from increased hydraulic fracture numbers.

Effect of surface roughness on methane adsorption in shale organic nanopores from the perspective of molecular simulations

Understanding the shale gas adsorption behavior within organic nanopores is crucial for its development. The pore surfaces commonly exhibit random inherent roughness, which can significantly impact gas adsorption, while it’s still in debate about whether the roughness could enhance the gas adsorption or not. To clarify the underlying mechanisms of the effect of surface roughness, in this study, the Grand Canonical Monte Carlo and Molecular Dynamic simulations are used to investigate the methane adsorption in wrinkled graphene nanochannels with various roughness. The heterogeneity of the adsorption layer along the rough topology is described in detail. The adsorption is enhanced near the concave region and weakened near the convex region. Additionally, increased surface roughness shifts the gas molecules’ aggregation modes in the concave region from a gradual gradient to oscillatory “adsorption points”. We observe that wall roughness due to the wrinkle can enhance the adsorption amount overall, attributed to the increased surface area and the increased unit adsorption capacity due to potential energy overlap. However, the contributions of both factors differ under various pressure and roughness conditions. Then a new mathematical method to estimate the gas adsorption amounts is provided and aligns well with molecular simulation results for a wide range of pressure and roughness conditions. And a potential answer the current debate about the positive or negative impact of adsorption due to roughness is promoted. The key may lie in the dominant role of the co-existed roughness caused by the fragment basal or edge surface. Our work could shed light on the underlying effect of surface roughness on the gas adsorption in shale, and have significant implications for the reserve estimation of shale gas formations.

Sparse-spike seismic inversion with semismooth newton algorithm solver

Seismic prospecting has been widely used in the exploration and development of underground geological resources, such as mineral products (e.x., coal, uranium deposit), oil and gas, groundwater, and so forth. Seismic impedance is a physical characteristic parameter of underground formation, which can be used in lithologic classification, rock characterization, stratigraphic correlation, and further mineral reservoir prediction, reserve estimation, and so forth. To estimate impedance from seismic data, one must perform reflectivity series inversion first. Under a simple exponential integration transformation, the reflectivity series can give the final estimated impedance. Sparse-spike seismic inversion is the most common method to obtain reflectivity series with high resolution. It adopts a sparse regularization to impose sparsity on reflectivity series. From sparse reflectivity series, the final estimated impedance has blocky features to make formation interfaces and geological edges precise, which is very important to accurately delineate the distribution range of mineral resources. The development of sparse-spike seismic inversion is still facing major challenges of fast optimization algorithms in real-life application, especially for massive seismic data in 3D case. Semismooth Newton algorithm (SNA), which is a second order mehtod and has super-linear, even quadratic convergence rate, is used to solve sparse-spike seismic inversion. The proposed algorithm has been compared with common used algorithms through a synthetic seismic trace and a 3D real seismic data volume. The results show that the proposed algorithm has faster convergence rate and fewer computation time. It provides a new effective algorithm to solve sparse-spike seismic inversion.

تحسين علاقة المرهون لضغط نقطة الفقاعة للنفط الخام الليبي

Accurate estimation of crude oil bubble point pressure plays a vital role in many petroleum engineering calculations, such as reserve estimation, material balance, reservoir simulation, production equipment design, and optimization of well performance. The Pb can be measured in the pressure-volume-temperature (PVT) experiments. Nonetheless, the PVT measurements have limitations, such as being costly and time-consuming. In this paper, the Al-Marhoun correlation for bubble point pressure for black oil reservoirs is improved using the linearization of non-linear regression techniques to fit Libyan crudes accurately. A total of 62 PVT data reports taken from various Libyan oil fields were used in the study. The PVT data consist of an oil gravity range of 24.7 ºAPI to 46.8 ºAPI and bubble point pressures of 123 psig to 6100 psig. Statistical error analyses and graphical methods were used to evaluate the original and modified Al-Marhoun correlations. The results showed that the improved Al-Marhoun correlation exhibits significantly lower average absolute error and deviation than the published ones. The correlation coefficient of R2 of the improved Al-Marhoun correlation is 96.70%, and the average percent error (APE) is 0.70% with a standard deviation (SD) of 14.70.

Compositing and regularization of drillhole data for geostatistical resource estimation

Compositing or regularization of drillhole data is common practice in mineral resource estimation and is deemed a necessary step in producing unbiased estimates of mineral resources and reserves. Commonly, data are collected over irregular distances due to the varying relative thicknesses of lithologies drilled or sampling/assaying strategies. This necessitates data transformation to regular lengths of equal size to ensure that all data have the same sample support. However, there have been few detailed publications on the effect of this process on the composited data that are subsequently taken forward for the estimation process. In this paper, three currently available compositing methods are reviewed and the effects of inappropriate compositing methodologies presented. It is shown through a case study that compositing samples to different lengths leads to changes in the average and variance of the grades in the drillcores in the dataset, which will impact the final estimated value. These differences are exacerbated by breaks or gaps in data where, for a variety of reasons, there has been no data collection or data have been lost. The importance of appropriately treating blank and zero data is also presented. Globally, these differences might be minimal, but locally may be substantial, affecting the efficiency of the estimation and subsequent use of the results in, for example, mine planning and reconciliation. Further detailed investigation of compositing practices is required if the full implications of compositing are to be understood and any induced bias effectively defined.

Advancements in field development planning through mathematical analysis for reserves estimation, casing design, accidental events and carbon dioxide storage

The objective of this work is to introduce novel methodologies in specific parts of Field Development Plans (FDPs) by leveraging contemporary technological advancements, industry best practices, and accumulated knowledge. The proposed methodologies comprise volumetric analysis by conducting stochastic simulations within a Monte Carlo framework to evaluate the associated uncertainties and a piston-like CO2 storage scheme (based on the material balance approach) aiming to enhance natural gas production and at the same time store CO2, mitigating climate change. Optimization of drilling operations based on appropriate material selection from commercial casing tables and assignment of a failure probability of casing configuration are also included. Another proposed methodology involves spatial risk analysis simulating accidental events based on the points of interest (POIs) and the distance from the event. Finally, a stochastic analysis of project economics is conducted showing uncertainties of cashflows throughout the economic life of the project and assessing the outcomes using various probabilistic performance metrics. The integration of these methodologies offers a robust framework for enhancing the effectiveness of FDPs and their applications, aligning with the evolving demands and complexities of the oil and gas sector. Such methodologies, rooted in mathematical tools aiming to quantify uncertainty in various ways, can increase production, potentially reducing costs through carbon tax incentives and benefit the environment. The applicability of the proposed methodology is demonstrated at the Frigg field, a natural gas reservoir situated in the Norwegian sector of the North Sea. Given its historical development in the 1970s and 1980s, the Frigg field serves as a rich repository of data and information for comparative analysis and fine-tuning of the proposed methodologies. The present study bridges the gap between theoretical advancements and practical applications, promoting innovation and efficiency within the oil and gas field development processes.

Current and Emerging Approaches for Primary Prevention of Coronary Artery Disease Using Cardiac Computed Tomography.

To summarize the current use of cardiac computed tomography (CT) technologies as well as their pertinent evidence in regards to prevention of coronary artery disease (CAD). Cardiac CTA has now become a main non-invasive method for the evaluation of symptomatic CAD. In addition to coronary calcium score, other CT technologies such as atherosclerotic plaque analysis, fractional flow reserve estimation by CT, pericoronary fat attenuation, and endothelial wall shear stress have emerged. Whether the use of CT modalities can enhance risk prediction and prevention in CAD has not been fully answered. We discuss the evidence for coronary artery calcium scoring and coronary CT angiography in primary prevention and the current barriers to their use. We attempt to delineate what can be done to expand use and what studies are needed to broaden adoption in the future. We also examine the potential roles of emerging CT technologies. Finally, we describe potential clinical approaches to prevention that would incorporate cardiac CT technologies.

Risk Management of Insurance Companies With The Implementation Of The Munich Chain Ladder Method In Claim Reserve Estimation

Risk Management of Insurance Companies With The Implementation Of The Munich Chain Ladder Method In Claim Reserve Estimation

Estimation of CO2 storage capacities in saline aquifers using material balance

Estimating CO2 storage capacities is crucial in developing carbon capture and storage projects. Material balance equation (MBE) methods, widely employed for oil and gas reserve estimation, offer a direct approach to estimating CO2 storage capacities. However, previous MBE methods rely on an original fluid in-place volume calculated using volumetric methods to estimate CO2 storage capacities, lacking validation for accuracy. It is essential to accurately estimate the original fluid in-place volume, representing the pore volume, as it substantially influences CO2 storage capacity. This study presents a refined MBE method that ensures accurate estimates of CO2 storage capacities by validating the original fluid in-place volumes in saline aquifers. The accuracy of this method was evaluated by comparing it with a commercial reservoir simulator for a synthetic aquifer example and the Sleipner L9 model. In the synthetic aquifer example, the relative error in CO2 storage capacity estimation with the proposed MBE method was only 2.09%, even when short-term (1-year) injection data were utilized. The proposed MBE method demonstrates consistent accuracy in estimating CO2 storage capacities under different aquifer properties, operating conditions, and MBE-related conditions. The proposed MBE method also accurately estimated the CO2 storage capacity in the Sleipner L9 model, achieving a relative error of 3.47%.

Non-invasive fractional flow reserve estimation in coronary arteries using angiographic images

Coronary artery disease is the leading global cause of mortality and Fractional Flow Reserve (FFR) is widely regarded as the gold standard for assessing coronary artery stenosis severity. However, due to the limitations of invasive FFR measurements, there is a pressing need for a highly accurate virtual FFR calculation framework. Additionally, it’s essential to consider local haemodynamic factors such as time-averaged wall shear stress (TAWSS), which play a critical role in advancement of atherosclerosis. This study introduces an innovative FFR computation method that involves creating five patient-specific geometries from two-dimensional coronary angiography images and conducting numerical simulations using computational fluid dynamics with a three-element Windkessel model boundary condition at the outlet to predict haemodynamic distribution. Furthermore, four distinct boundary condition methodologies are applied to each geometry for comprehensive analysis. Several haemodynamic features, including velocity, pressure, TAWSS, and oscillatory shear index are investigated and compared for each case. Results show that models with average boundary conditions can predict FFR values accurately and observed errors between invasive FFR and virtual FFR are found to be less than 5%.

Development of a machine learning algorithm to predict the residual cognitive reserve index.

Elucidating the mechanisms by which late-life neurodegeneration causes cognitive decline requires understanding why some individuals are more resilient than others to the effects of brain change on cognition (cognitive reserve). Currently, there is no way of measuring cognitive reserve that is valid (e.g. capable of moderating brain-cognition associations), widely accessible (e.g. does not require neuroimaging and large sample sizes), and able to provide insight into resilience-promoting mechanisms. To address these limitations, this study sought to determine whether a machine learning approach to combining standard clinical variables could (i) predict a residual-based cognitive reserve criterion standard and (ii) prospectively moderate brain-cognition associations. In a training sample combining data from the University of California (UC) Davis and the Alzheimer's Disease Neuroimaging Initiative-2 (ADNI-2) cohort (N = 1665), we operationalized cognitive reserve using an MRI-based residual approach. An eXtreme Gradient Boosting machine learning algorithm was trained to predict this residual reserve index (RRI) using three models: Minimal (basic clinical data, such as age, education, anthropometrics, and blood pressure), Extended (Minimal model plus cognitive screening, word reading, and depression measures), and Full [Extended model plus Clinical Dementia Rating (CDR) and Everyday Cognition (ECog) scale]. External validation was performed in an independent sample of ADNI 1/3/GO participants (N = 1640), which examined whether the effects of brain change on cognitive change were moderated by the machine learning models' cognitive reserve estimates. The three machine learning models differed in their accuracy and validity. The Minimal model did not correlate strongly with the criterion standard (r = 0.23) and did not moderate the effects of brain change on cognitive change. In contrast, the Extended and Full models were modestly correlated with the criterion standard (r = 0.49 and 0.54, respectively) and prospectively moderated longitudinal brain-cognition associations, outperforming other cognitive reserve proxies (education, word reading). The primary difference between the Minimal model-which did not perform well as a measure of cognitive reserve-and the Extended and Full models-which demonstrated good accuracy and validity-is the lack of cognitive performance and informant-report data in the Minimal model. This suggests that basic clinical variables like anthropometrics, vital signs, and demographics are not sufficient for estimating cognitive reserve. Rather, the most accurate and valid estimates of cognitive reserve were obtained when cognitive performance data-ideally augmented by informant-reported functioning-was used. These results indicate that a dynamic and accessible proxy for cognitive reserve can be generated for individuals without neuroimaging data and gives some insight into factors that may promote resilience.

Geostatistical Models and Estimation of Laterite Nickel Ore Reserves, Marombo Block, South Sulawesi

Abstract Geostatistical modeling in this study requires a distribution of drill points that have a fixed dimension so that they can produce a reserve accuracy match that is the same as the variograph. The choice of reserve estimation method is prioritized by determining the geological domains of limonite, saprolite and bedrock through a geostatistical approach, namely ordinary krigging (OK) and inverse distance weight (IDW) with the JORC standard (2012), for the accuracy of the limonite and saprolite zone boundaries. The reserve model has shown a definite model of the limonite and saprolite zones and can be used further. By using simulations on cut-off grade (COG) to calculate resources and classify mineral resources, the estimation results obtained mean nickel levels above 1.40% so that the CoG to be used must be greater than Ni 0.90% for limonite, and Ni 1.10% for saprolite. The results of modeling the geometry of laterite nickel ore obtained that the total tonnage shown in limonite was 0.36 wmt (wet metric ton), inferred 0.10 wmt, and indicated in saprolite was 0.58 wmt, inferred was 0.40 wmt.

FOREST FOOD RESOURCES IN THE SAKHA REPUBLIC (YAKUTIA)

According to the “Ecosystem Services of Russia” Prototype of National Report (2016), data on terrestrial ecosystem resources in Russia are fragmented. The purpose of this work is to fill the existing gaps in the knowledge of the biodiversity of plant species and the sustainability of traditional food systems across the economic zones of the Sakha Republic (Yakutia). Materials and methods. Although the range of edible plants in Yakutia counts more than 100 species, only nine species of berries, eight species of mushrooms, and three species of other food plants were classified as resource-significant based on the consolidated archive materials of the IBPC at FRC YaSC SB RAS and the results of the field studies carried out in 2002-2019. This list of twenty species was tested via a socio-economic survey undertaken as part of the RISE project in 2021-2023. The study was conducted with the permission of the local biomedical ethics committee of the Medical Institute of the North-Eastern Federal University. Each participant signed an informed consent form to be surveyed, according to the World Medical Association’s Declaration of Helsinki, which regulates the research involving human subjects. The study embraced 400 rural households. Results. The data on biological stocks of resource-significant food species of Yakutia’s forests were presented based on the consolidated archive materials and results of the field studies undertaken by the IBPC at FRC YaSC SB RAS. Coupled research methods of natural and social sciences were used to produce the estimates of the economic reserves of berries and mushrooms across economic zones of the Sakha Republic (Yakutia) and the volumes of their harvesting by rural households and the population of the region in general. Conclusion. Social methods have proven reliable for studying biodiversity in a large-sized region. They allow to fill the gaps in the databases built on the results of field studies, particularly in terms of popularity and, accordingly, the prevalence of certain food species. The higher productivity and diversity of flora in Southern Yakutia were confirmed compared to the Arctic and other economic zones. Funding. The reported study was performed under the e-Asia Climate Call and funded by RFBR, project number 21-55-70104, and project VI.52.1.8. “Theoretical and applied aspects of investigation the diversity of the vegetation of Northern and Central Yakutia (East Siberia)” 0376-2016-0001; registration number АААА-А17-117020110056-0.