Reserve Estimation Research Articles

Rapid history matching of petroleum production from well logs and 4D seismic via Machine Learning techniques in the Norne Field, offshore Norway

Predicting oil and gas reservoir behaviour requires multiple property modelling using various formulae, relationships and empirical techniques, which is time-consuming and often ineffective in precisely capturing non-linear dependencies. Artificial Intelligence (AI) or Machine Learning (ML) techniques can build time-series models for modelling dynamic reservoir properties such as water, oil and gas saturation, and pressure, thus capturing changes caused by hydrocarbon production. Here, 4D (time lapse) seismic surveys have been used to model the changes in water saturation using AI techniques such as multi-linear regression, multi-variable kriging and random forest. Statistical testing of the resulting 3D reservoir models using R-Squared, RMSE (root-mean-square error) and MAPE (mean absolute percentage error) indicated the random forest technique gave the best results and stratification had a negligible effect. Increasing the training size set from 10% to 80% improved the statistics as expected though the rate of improvement falls rapidly above a training dataset size of 40%. This indicates that 3D models with good accuracy could be obtained even with limited data. Similar techniques can be run to build 3D time-series pressure models and the results can be used for improved history matching, forward estimation of production data and estimation of reserves.

Integrating Petrophysical Analysis and Seismic Interpretation for Oil and Gas Reserves Estimation in “TAU” Field, Jatibarang Sub–Basin, North West Java Basin

The focus of the study is the North West Java Basin, specifically the Jatibarang Sub Basin within it. The research aims to identify new potential areas for hydrocarbon reserves using seismic interpretation, and tetrophysical analysis, also calculate the volume of reserves in those areas. According to the study, 2 potential areas for hydrocarbon reserves were identified in the Parigi Formation, 2 were identified in the Baturaja Formation, 3 were identified in the Talang Akar Formation, and 1 was identified in the Jatibarang Formation. No new potential areas were discovered in the Cibulakan Formation. The study used petrophysical interpretation of six wells to determine the average values of shale volume (9.57% to 29.87%), effective porosity (19.45% to 21.67%), and water saturation (69.73% to 93.50%) for each potential zone. The TAU-5 well in the Baturaja Formation (Zone 1, Zone 2, Zone 3, and Zone 4) was determined to be the most economically viable zone for production based on the values of shale volume, porosity, and water saturation. The volume of hydrocarbon reserves in the BR-1 potential zone was calculated to be 182.741281 MBO in OOIP and 177.85122 BCF in OGIP. The BR-2 potential zone had an OOIP value of 1,197.534148 MBO and an OGIP of 1165.488764 BCF.

Estimation of body condition score change in dairy cows in a seasonal calving pasture-based system using routinely available milk mid-infrared spectra and machine learning techniques

Body condition score (BCS) is a subjective estimate of body reserves in cows. Body condition score and its change in early lactation have been associated with cow fertility and health. The aim of the present study was to estimate change in BCS (ΔBCS) using mid-infrared spectra of the milk, with a particular focus on estimating ΔBCS in cows losing BCS at the fastest rate (i.e., the cows most of interest to the producer). A total of 73,193 BCS records (scale 1 to 5) from 6,572 cows were recorded. Daily BCS was interpolated from cubic splines fitted through the BCS records, and subsequently used to calculate daily ΔBCS. Body condition score change records were merged with milk mid-infrared spectra recorded on the same week. Both morning (a.m.) and evening (p.m.) spectra were available. Two different statistical methods were used to estimate ΔBCS: partial least squares regression and a neural network (NN). Several combinations of variables were included as model features, such as days in milk (DIM) only, a.m. spectra only and DIM, p.m. spectra only and DIM, and a.m. and p.m. spectra as well as DIM. The data used to estimate ΔBCS were either based on the first 120 DIM or all 305 DIM. Daily ΔBCS had a standard deviation of 1.65 × 10-3 BCS units in the 305 DIM data set and of 1.98 × 10-3 BCS units in the 120 DIM data set. Each data set was divided into 4 sub-data sets, 3 of which were used for training the prediction model and the fourth to test it. This process was repeated until all the sub-data sets were considered as the test data set once. Using all 305 DIM, the lowest root mean square error of validation (RMSEV; 0.96 × 10-3 BCS units) and the strongest correlation between actual and estimated ΔBCS (0.82) was achieved with NN using a.m. and p.m. spectra and DIM. Using the 120 DIM data, the lowest RMSEV (0.98 × 10-3 BCS units) and the strongest correlation between actual and estimated ΔBCS (0.87) was achieved with NN using DIM and either a.m. spectra only or a.m. and p.m. spectra together. The RMSEV for records in the lowest 2.5% ΔBCS percentile per DIM in early lactation was reduced up to a maximum of 13% when spectra and DIM were both considered in the model compared with a model that considered just DIM. The performance of the NN using DIM and a.m. spectra only with the 120 DIM data was robust across different strata of farm, parity, year of sampling, and breed. Results from the present study demonstrate the ability of mid-infrared spectra of milk coupled with machine learning techniques to estimate ΔBCS; specifically, the inclusion of spectral data reduced the RMSEV over and above using DIM alone, particularly for cows losing BCS at the fastest rate. This approach can be used to routinely generate estimates of ΔBCS that can subsequently be used for farm decisions.

Spatial distribution and reserve estimation of sand and gravel deposits using geostatistical methods in west Basrah, southern Iraq

Sand and gravel are among the most common natural materials. They are generally used as a raw material in concrete, road construction, mixing with asphalt and filling material, among other uses. Sand and gravel deposits are widespread within the southern and central regions of Iraq. Among these deposits are the alluvial sand and gravel that form the upper part of the Dibdibba Formation in the Al-Rafiya, Al-Tuba, Chwaibda and Safwan blocks in west Basrah, southern Iraq. In this research, grain size distribution for channel samples collected from each block was determined. Thickness of overburden and industrial beds was measured and the stripping ratio (SR) was calculated. Geostatistical interpolation approaches including inverse distance weighting (IDW) and ordinary kriging (OK) were utilised to predict the spatial distribution of sand and gravel deposits and their reserves from borehole data. Data validation using the mean error (ME) and root-mean-square error (RMSE) were applied. The deposits can be characterised as gravelly sand (gS) in the Al-Tuba, Al-Rafiya and Chwaibda blocks, while it is a sandy gravel (sG) in the Safwan block. The overburden gypcrete beds varied between 1 and 3 ​m thick, while the industrial sand and gravel beds which appear as planar and sometimes lenticular deposits ranging between 3 and 8 ​m thick. The SR is generally low, considering the easily removed lightweight material of the overburden bed and the easily extractable fairly flat and near-surface friable sand and gravel deposits. Reserves are estimated as 376.63 Million m3 and 385.97 Million m3 using the IDW and OK approaches, respectively. Based on the least error values of ME and RMSE, we conclude that the OK method is the more accurate spatial interpolation approach for sand and gravel reserve estimation in all blocks compared to the IDW method.

Residual oil saturation investigation in Barra Velha Formation reservoirs from the Santos Basin, Offshore Brazil: A sedimentological approach

Residual oil saturation (Sor) is a petrophysical parameter of great relevance, as it is used for flow simulation, production forecast, and reserve estimation. However, oil trapping is rarely investigated from a geological point of view. This work integrates petrography, RCAL, SCAL, and X-ray μCT to understand how lithological characteristics control Sor in the lacustrine carbonate reservoirs of the Pre-salt section of the Santos Basin, offshore eastern Brazil. The in-situ facies, composed of calcite shrubs and spherulites, and magnesian clays, have Sor directly related to the diagenetic evolution of the Mg-clays, including processes of replacement by dolomite and dissolution and their relationships to the calcite shrubs and spherulites. The intense replacement of the clay matrix by dolomite, followed by dissolution, developed intercrystalline porosity with a small pore-to-throat ratio, favoring water sweeping. On the other hand, the preservation of primary porosity among shrubs or the development of large matrix dissolution pores favors oil trapping. In intraclastic rocks made by redeposited fragments of shrubs and spherulites, the main control of Sor is depositional texture: rocks with larger grain size tend to have greater porosity heterogeneity and, consequently more oil trapped. On the other hand, fine-grained calcarenites tend to have a more homogeneous porous medium, which enables oil displacement. Finally, the imaging of Sor in reservoir conditions indicates mixed wettability. However, there is no clear relationship between Sor and predominant mineralogy, which should be investigated in future works.

Separation, reserve estimation and radioactivity responsibility of the economic heavy minerals of East El- Arish black sand, North Sinai, Egypt

Multi-heavy mineral separation techniques like density, magnetic, and electromagnetic techniques are followed by picking, grain counting, and mineralogical examinations conducted with an environmental scanning electron microscope (ESEM). These techniques have been applied to several drill hole (well) composite samples representing beach sand and dunes of East El-Arish area, North Sinai, Egypt. The results explain the remarkable enrichment in total heavy minerals (THM) of beach sand (4.4%) compared to sand dunes (2.6%): this is due to the natural concentration of the currents of the Mediterranean Sea. After characterizing the heavy minerals in terms of the abundance of each mineral species, the mean values of content relative to total economic heavy minerals (TEHM) were determined as 70.03% ilmenite, 3.81% leucoxene, 3.03% magnetite, 8.7% garnet, 10.6% zircon, 3.13% rutile, 0.37% monazite, and 0.31% titanite in the sand dune samples. The total economic heavy minerals in the beach samples were 64.08% ilmenite, 1.6% leucoxene, 2.84% magnetite, 18.16% garnet, 10.4% zircon, 2.18% rutile, 0.61% monazite, and 0.13% titanite. Monazite, thorite, zircon, and apatite are the main radioactive minerals in the study area. The combination of two specific characteristics of the studied monazite (relatively high Th-U content and high frequency) makes monazite the main contributor to radioactivity of the study area.

Surgical management of colorectal liver metastases—a practical clinical approach

Summary Background Despite the remarkable advances that have been made in the management of colorectal cancer over recent years, the optimal treatment for colorectal liver metastases (CRLM) remains a controversial matter. Undoubtedly, such a complex disease requires a multidisciplinary approach, in which close collaboration between all specialists involved in its management is of utmost importance. Methods A literature search was conducted in PubMed. There was no limit set to the date of publication. The main focus of the literature review is to provide a comprehensive summary of the current multidisciplinary management of CRLM while highlighting the surgical approach. Results Assessment of resectability, evaluation of the patient’s fitness, and discussion of clinicopathological features all play a vital role in finding the most suitable treatment strategy for CRLM patients, who should all be timely discussed in the multidisciplinary tumor board, in order to decide upon the optimal therapy sequence, operative time window, and postoperative treatment. Conclusion Although hepatic resection remains the only potentially curative treatment strategy for patients with CRLM, a multidisciplinary approach is essential for optimal treatment. A clear definition of treatment goal (curative vs. palliative) at the time of disease diagnosis determines the further therapeutic course. Preoperative estimation of liver functional reserve is a key factor in the decision-making process for CRLM resectability.

Machine Learning-Based Delineation of Geodomain Boundaries: A Proof-of-Concept Study Using Data from the Witwatersrand Goldfields

Machine-aided geological interpretation provides an opportunity for rapid and data-driven decision-making. In disciplines such as geostatistics, the integration of machine learning has the potential to improve the reliability of mineral resources and ore reserve estimates. In this study, inspired by existing geostatistical approaches that use radial basis functions to delineate domain boundaries, we reformulate the problem into a machine learning task for automated domain boundary delineation to partition the orebody. We use an actual dataset from an operating mine (Driefontein gold mine, Witwatersrand Basin in South Africa) to showcase our new method. Using various machine learning algorithms, domain boundaries were created. We show that based on a combination of in-discipline requirements and heuristic reasoning, some algorithms/models may be more desirable than others, beyond merely cross-validation performance metrics. In particular, the support vector machine algorithm yielded simple (low boundary complexity) but geologically realistic and feasible domain boundaries. In addition to the empirical results, the support vector machine algorithm is also functionally the most resemblant of current approaches that makes use of radial basis functions. The delineated domains were subsequently used to demonstrate the effectiveness of domain delineation by comparing domain-based estimation versus non-domain-based estimation using an identical automated workflow. Analysis of estimation results indicate that domain-based estimation is more likely to result in better metal reconciliation as compared with non-domained based estimation. Through the adoption of the machine learning framework, we realized several benefits including: uncertainty quantification; domain boundary complexity tuning; automation; dynamic updates of models using new data; and simple integration with existing machine learning-based workflows.

Estimation of Functional Reserve in Patients with Hospital-Associated Deconditioning

Estimation of Functional Reserve in Patients with Hospital-Associated Deconditioning

INTEGRATED PETROLEUM RESERVOIR CHARACTERIZATION. THE CASE OF ZHAO57 BLOCK (Es4 + EK1)

This paper put forward the result of a case studied from a reservoir characterization carried out on Es4+Ek1 reservoir of the Zhao57 block, Zhaozhouqiao oil field, Hebei Province, China. The objective of the study was to accurately create a reservoir model of the oil field and use it to forecast oil and gas production. To achieve this, we characterized the formation by applying integrated geologic and engineering data for the purpose of providing insight understanding of controls on oil and gas production. The formation was first divided into seven groups and fifty four small layers, followed by stratigraphic correlations and quantification of sand thickness. The fifth group was found to be the main oil bearing formation, its top mainly comprise of siltstone while the middle and the bottom comprise mainly of thick layer of massive sand. Sandstone density presents decreasing trend from northwest to south east. Structure model on the fifth group depicts a series of faulting which is believed to be a contributing factor on oil and gas distribution in the group. Average porosity on the fifth group is found to be 7.54%, average permeability is as low as 2.6mD and reserve estimation recorded a value of 82.5364x104t with the V-12 and V-13 layers showing the highest reserve. Result derived from this study will no doubt be useful in respect of further development of the field especially during secondary and tatiary recoveries.

An integrated approach for aquifer characterization and evaluation in a complex geologic terrain (Cross River State, Nigeria): A contribution to support sustainable development and management of groundwater

Increasing agriculture, industrial and urban growth have resulted in the rapid and an haphazard development of groundwater resources through indiscriminate abstraction in the Cross River State (CRS), Nigeria. This has resulted in stress on groundwater, especially in areas with complicated geologic formations where reliable data are lacking. This paper focuses on understanding the hydrogeological characteristics of the different aquifers using an integrated approach. The methods applied are hydrogeological surveys together with interpretation of geological, geoelectrical and hydrochemical data. The results reveal six hydrogeological zones (HZs): HZs 1 (igneous and metamorphic rocks), 2 (sandstone and siltstone), 3 (massive, baked and fractured shale), 4 (sand, gravel, silt and clay), 5 (carbonate rocks) and 6 (basic intrusive rocks). Estimates of groundwater reserves indicate that the HZ 1 has the largest reservoir of stored fresh groundwater, estimated at 332,637.00 × 106 m3, while the highest yield is estimated to be 1085.53 m3/d from HZ 4. Groundwater chemistry showed that Na+ and Ca2+ dominated the cations, while Cl− and HCO3− dominated the anions. In general, major chemistry of the water is controlled by atmospheric input, weathering of silicate, carbonate minerals and ion exchange. In terms of drinking and irrigation quality, the groundwater showed low values of total dissolved solids, pH, hardness, dissolved oxygen and NO3− at few locations, while more than 50% of the samples have excellent quality for irrigation use. However, higher values were recorded in HZ 2, due to occurrence of brackish water.

Machine learning-based inversion for acoustic impedance with large synthetic training data: Workflow and data characterization

Where wells are sparse or training data are difficult to label with high-quality wireline-derived impedance logs, machine learning (ML)-based inversion of acoustic impedance typically depends on small training data sets, leading to biased prediction. We have advanced a novel workflow that applies large synthetic seismic training data to reduce facies-related bias. Using a geologically realistic model as the truth model, we randomly select sparse seed wells to perform sequential Gaussian simulation (SGS) for impedance models of the same geometry and simulate facies variability. We implement random forest regression on 30 features extracted from the synthetic volume. We observe that more seed wells tend to reduce facies-induced bias by sampling more types of facies, resulting in a better prediction. We then focus on the responses of SGS models to facies changes, the number of seed wells necessary for a useful synthetic model, and how much a synthetic model can help ML-based inversion. We observe that the SGS synthetic training model outperforms well-direct training in general. For modeled clastic shore-zone systems in Miocene Gulf of Mexico, two or more seed wells are necessary for a significant reduction of root-mean-square error and outliners, and improvement of facies imaging. In a field-data test, we apply a similar workflow to quantitatively predict acoustic impedance, which is then converted to a sand-volume map at a high-frequency sequence (10–100 m), revealing detailed facies and sandstone patterns. Such results are valuable in many geologic and engineering applications, such as hydrocarbon and CO2 reservoir prospecting, reserve estimation, simulation, etc.

A two-phase type-curve method with multiscale fluid transport mechanisms in hydraulically fractured shale reservoirs

The quantitative understanding of hydraulic fracture (HF) properties guides accurate production forecasts and reserve estimation. Type curve is a powerful technique to characterize HF and reservoir properties from flowback and long-term production data. However, two-phase flow of water and hydrocarbon after an HF stimulation together with the complex transport mechanisms in shale nanopores exacerbate the nonlinearity of the transport equation, causing errors in type-curve analysis. Accordingly, we propose a new two-phase type-curve method to estimate HF properties, such as HF volume and permeability of fracture, through the analysis of flowback data of multi-fractured shale wells. The proposed type curve is based on a semianalytical solution that couples the two-phase flow from the matrix with the flow in HF by incorporating matrix influx, slippage effect, stress dependence, and the spatial variation of fluid properties in inorganic and organic pores. For the first time, multiple fluid transport mechanisms are considered into two-phase type-curve analysis for shale reservoirs. We analyze the flowback data from a multi-fractured horizontal well in a shale gas reservoir to verify the field application of the proposed method. The results show that the fracture properties calculated by the type-curve method are in good agreement with the long-time production data.

Extra-Basal Ganglia Brain Structures Are Related to Motor Reserve in Parkinson's Disease.

The "motor reserve" is an emerging concept based on the discrepancy between the severity of parkinsonism and dopaminergic degeneration; however, the related brain structures have not yet been elucidated. We investigated brain structures relevant to the motor reserve in Parkinson's disease (PD) in this study. Patients with drug-naïve, early PD were enrolled, who then underwent dopamine transporter (DAT) scan and diffusion tensor imaging (DTI). The severity of motor symptoms was evaluated with the Unified Parkinson's Disease Rating Scale score of bradykinesia and rigidity on the more affected side and dopaminergic degeneration of DAT uptake of the more affected putamen. Individual motor reserve estimate (MRE) was evaluated based on the discrepancy between the severity of motor symptoms and dopaminergic degeneration. Using DTI and the Brainnetome atlas, brain structures correlated with MRE were identified. We enrolled 193 patients with drug-naïve PD (mean disease duration of 15.6±13.2 months), and the MRE successfully predicted the increase of levodopa equivalent dose after two years. In the DTI analysis, fractional anisotropy values of medial, inferior frontal, and temporal lobes, limbic structures, nucleus accumbens, and thalamus were positively correlated with the MRE, while no brain structures were correlated with mean diffusivity. Additionally, degree centrality derived from the structural connectivity of the frontal and temporal lobes and limbic structures was positively correlated with the MRE. Our results show empirical evidence for MR in PD and brain structures relevant to MR, particularly, the extra-basal ganglia system including the limbic and frontal structures.

Discovery of anomalous molybdenum enrichment in Ordovician and Silurian stone coal: Relevance, origin and recommendations

Molybdenum (Mo) is a strategic element but has a notably low concentration at the Earth's surface. Consequently, competition for molybdenum resources at the national strategic level has begun to emerge, and in recent years, large-scale mining has led to the gradual depletion of molybdenum deposit resources. Here, thirty-four element enrichment patterns of Ordovician and Silurian stone coals in central China are reported. Molybdenum is the most enriched element, with an average of 208 mg/kg (58.2–440 mg/kg), which is 99 times the global hard coal average, and this molybdenum enrichment is associated with Ba–Ga–U–Cr–Na–K–-Cu–Se–Zn enrichment and elevated SiO2, CaO, K2O, MgO, Na2O, MnO and P2O5 concentrations. These analyses reveal four stone coal samples with molybdenum concentrations of 260, 312, 403 and 440 mg/kg, which meet the grade for the molybdenum mineral exploitation formulated standard, indicating that the Ordovician and Silurian stone coal deposits should be considered promising alternative sources of molybdenum. The crude reserve estimate of molybdenum is approximately 29.2 × 104 tons. The anomalous molybdenum in the studied stone coal was sourced from a complex combination of hydrothermal fluids, original biomass and terrigenous materials. The unique paleogeographic location and geological structure in central China resulted in the anomalous molybdenum concentrations in the stone coal forming at that time, producing a unique type of coal-hosted molybdenum deposit. Future studies will consider the ecological effects of the molybdenum extraction mode and the cost savings effects of extracting molybdenum from stone coal. A comprehensive utilization plan is also needed. Next, a thorough study of molybdenum exploration related to black rock series must be performed to increase the total molybdenum resources and support Chinese international competitiveness.

A calculation model for the spatial distribution and reserves of ground ice - A case study of the Northeast China permafrost area

Ground ice is solid water that exists in permafrost. The spatial distribution and reserve estimation of ground ice has been a challenging issue in permafrost studies. A model for mapping the spatial distribution and calculating the reserve of ground ice was developed. By using the borehole data and soil parameters, the strata in different geomorphic units were classified into, from top to bottom, cultivated soil, clay, silty clay, sandy soil, gravel soil, and bedrock. The ground ice calculation range in its extent and depth was determined for different geomorphic units. The thickness and reserve of ground ice were obtained by using the material composition of soil theory and the GIS spatial calculation method. The model results were compared with the survey data and showed 72.3% accuracy. For the Northeast China permafrost area, above 20 m depth, the average thickness of ground ice was 2.3 ± 1.1 m, and the reserve was 1045.5 km3. This work provides a framework to enable permafrost researchers to map the spatial distribution and estimate the reserve of ground ice. The results lay foundations for studies in climate disaster, hydrological cycle, succession of ecological environment, cold region engineering stability and so on.

Discovery of anomalous gallium enriched in stone coal: Significance, provenance and recommendations

Gallium (Ga) is a critical mineral that plays an irreplaceable role in consumer electronics, clean energy technologies and the aerospace industry. Nowadays competition for gallium resources at the national strategic level has begun to emerge, but gallium resources are unevenly distributed globally, and their presence is not guaranteed. New discoveries revealed an average gallium concentration of thirty-one samples from M1, M2, M3 and M4 stone coal-bearing seams of the Cambrian strata on South Qinling Orogenic Belt in central China is 157 mg/kg (9.98–747 mg/kg), which is 27.6-fold higher than the global hard coal average, as well as the existing association of Mo–V–U–Cd–Zn–Ba–Se–Mg–Ni–Cu enrichment. Ga average of these coal seams are 344 mg/kg (M3, 44.5–747 mg/kg, n = 11), 270 mg/kg (M4, 14.3–270 mg/kg, n = 5), 53.8 mg/kg (M2, 22.6–75.4 mg/kg, n = 8) and 19.8 mg/kg (M1, 9.98–34.9 mg/kg, n = 7) respectively, as well as the thickness of approximately 6, 12, 8, and 20 m, which be close to or exceed to the boundary grade standard (30 mg/kg) and minimum recoverable thickness (0.7 m) of gallium resources exploration. These findings indicate that the Cambrian stone coal deposits, especially in the middle and late Cambrian period, should be considered as promising alternative sources of gallium. The anomalous gallium-enriched sediments originated from a complex combination of hydrothermal fluids, original biomass and terrigenous materials. For the M1 stone coal-bearing seams, gallium most likely occurred in the mode of GaAs, GaxIn1-xAs and GaO(OH), while modes of gallium in the M2 to M4 seams is interrelated with the organic affinity and clay minerals. The crude reserve estimate of gallium resources in central China is approximately 10.06 × 104 tons, corresponding to a super-large coal-hosted gallium ore deposit. The unique paleogeographic location and geological structure in central China resulted that this newly discovered deposit is a unique type of gallium-enriched deposit that has been discovered worldwide. These discoveries will provide the critical parameters when developing distinctive beneficiation processes and appropriate extraction procedures, as well as guidance and effective for future prospecting regions of gallium resources around the globe, involving a combination consider the distribution of regional deep–large fault zones and the middle to late Cambrian black rock series deposits.

Mathematical Modification to the Classical Volumetric Method for Estimating Oil Reserves

Using fuzzy logic technique, this work proposes a mathematical adjustment to the classical volumetric method for estimating oil reserves to manage the level of uncertainty associated with oil reserves estimation. This technique introduces a risk factor (α) into the volumetric method equation to account for the uncertainty associated with estimating the parameters that are used in the volumetric method equation. Risk types that may affect oil reserves estimation can be considered using the risk factor (α) in the modified equation. Results showed that the amount of proven oil reserves decreases exponentially as the value of risk factor (α) increases. It also showed that the ratio of the expected proven oil reserves with respect to proven oil reserves (N*/N) goes to zero when the value of risk factor (α) reaches a value of (5). Three cases were proposed to categorize uncertainty in proven oil reserves estimation: high risk estimate, middle risk estimate and risk-free estimate. Results showed that, for the case of high-risk estimate, expected proven oil reserves (N*) was appreciably lower than the proven oil reserves (N) due to the inclusion of risk. Sources of risk may include, but not limited to, lack of expertise of the evaluator, level of integrity of the evaluator, engineering errors during measurement and calculation and governmental laws. Results also showed that the calculated amount of proven oil reserves (N*), for the middle risk estimate case, is much higher than that for high risk estimate case. As for the riskfree estimate case, the calculated proven oil reserves (N*) using the modified formula equals to the proven oil reserves (N) calculated by the classical volumetric formula. This case reflects 100% confidence and reliability in the parameters’ estimation which also places great trust in the evaluator’s integrity and expertise.

Preliminary application of grade equivalent theory in a local section of a mine and economic estimation of the deposit

By introducing the practical application of grade equivalent theory in a copper -cobalt mine, it can show the maximum economic value of the individual ore block and the mine, maximize the comprehensive utilization of limited resources and contains positive significance for extending the service life of the mine. Briefly introducing the steps of Micromine software in the process of reserve estimation and the feasibility of displaying the economic value of individual ore blocks. Estimation of reserve can be completed objectively and scientifically by Micromine software, grade and economic value of ore blocks can be displayed in three-dimensional image.

ВИКОРИСТАННЯ КОНТРОЛЬНИХ ПРОБ ДЛЯ ЗАБЕЗПЕЧЕННЯ І КОНТРОЛЮ ЯКОСТІ (QA/QC)

Іn geological exploration, the quality of the data underlying of resource and reserve estimation is critically important. According to various international regulations and current world best practices, Quality Assurance and Quality Control (QA/QC) programs are a necessary part of geological exploration. Quality Assurance (QA) is used to avoid the problems with quality, Quality Control (QC) is aimed at detecting them in case of their occurrence, and together they form the overall Quality System – QA/QC. The article considers the main stages of the history of QA/QC development, which begins in the Middle Ages and continues in our time. Control samples used to control analytical tests have different types and purposes / functions: standards, preparation blanks, coarse blanks, analytical blanks, field duplicates, coarse duplicates, pulp duplicates and umpire laboratory control. Their amount and ratio is the main topic of the research. According to the published data of nine different authors, the recommended control sample amount of each type is different. On average, the amount of control samples of all types is about 20% of the total number of routine samples. In order to highlight the current state of the issue, the authors of the article have analyzed 111 QA/QC programs for 87 ore projects according to the public reports disclosed by the issuers of the Toronto Stock Exchange. So, in practice, the control sample amount does not necessarily reach the recommended 20 % and is slightly more than 16 %. The main conclusions regarding the amount and ratio of control samples can be presented as follows: (1) the general increasing trend in the amount and variety of control samples is observed; (2) among the control sample types, the so-called standards are most often used, and the least used are coarse blanks; (3) the control sample amount and variety also depends on the mineral type; (4) at more advanced exploration stages of the project, the control sample relative number usually increases, but in some cases it may decrease if the results of the previous stages are satisfactory, and the methodology and laboratory are not changed.