Geological Feature Modeling and Reserve Estimation of Uranium Deposits Based on Multiple Interpolation Methods

Uranium resource distribution and accurate reserve evaluation are important references for mineral investment and production. Eight kinds of interpolation methods in the Groundwater Modeling System (GMS), including ordinary kriging (OK), are used in this study to predict the spatial distribution of reserve-related parameters, such as uranium grade, ore thickness and uranium content per square meter. The present study draws the following conclusions: (1) Cross-validation found that the uranium grade value using the spherical method is the closest to the actual value. The spherical method has the best interpolation effect. (2) The relative error, which is +3.62%, between the uranium reserves that is calculated by the spherical interpolation method and that by the traditional calculation value is the smallest. (3) The setting of the number of interpolation grids is related to the actual number of boreholes. The ratio between the two will affect the accuracy of reserve estimation, and different interpolation methods have different degrees of influence on reserve estimation. This method is applicable to all in-situ leaching sandstone uranium mines. Further study needs to be carried out toward heterogeneity of three-dimensional space, which will make the estimation more accurate.

MODEL STOKASTIK DENGAN PENDEKATAN GENERALIZED LINEAR MODEL UNTUK MENGESTIMASI CADANGAN KLAIM INCURRED BUT NOT REPORTED

One of the crucial things in the insurance business is determining the amount of IBNR claim reserves. The amount of IBNR's claim reserves is uncertain so it is necessary to estimate as accurately as possible. The estimation results of IBNR's claim reserves will affect the solvency and sustainability of the company. To calculate the estimated IBNR claim reserves, several approaches are used both deterministically and stochastically. This study uses a stochastic model with the GLM approach for data that is assumed to have an ODP distribution. Besides, this study also uses 2 different methods to calculate parameter estimates in the model, namely by performing parameter transformations and using the Verbeek algorithm. This study will compare the results of the IBNR claim reserve estimation obtained using these two methods in estimating the parameters in the model. The estimation results obtained indicate that the value of the IBNR claim reserves is the same. The advantage of the Verbeek algorithm is that the resulting parameter values ​​have interpretations.

Andesite prospect at West Sungkai of North Lampung: Its distribution based on electrical resistivity tomography

Infrastructure construction made andesite’s demand has increased, particularly in Lampung Province. In this research, its distribution in West Sungkai of North Lampung is mapped based on Electrical Resistivity Tomography (ERT) data from 6 lines, each of them was 186 m in length. Due to its excellent vertical resolution, Wenner configuration is performed. The research area is part of Quarter Holocene Volcanic (Qhv) formation. Lajur Barisan members consist of volcanic breccia, lava, and andesite-basalt tuff; thus, resistivity modeling is built within this aisle. Subsurface resistivity model has been created using the non-linear inversion method with promising low error at the third to fifth iterations, which marks an acceptable value. Using 2D and 3D ERT modeling, it is estimated that there are three mains of rocks based on their resistivity value: sandy tuff with 65 – 212 Ω m; tuff with 212 – 655 Ω m; and andesite with resistivity more than 655 Ω m. Andesite within this area is likely lava andesite which spread from the middle to the West and north. It is located at 5 – 35 m in depths with the reserve estimation of andesite is about 1.65 million tons.

An Approach to Estimating Reserve Volumes by Discounting Unauthorized Production Via Crude Oil Theft

Reserves are typically estimated and re-validated throughout the life of a producing field. The accuracy of this estimation is based on the availability of relevant and current data from that reservoir or field and other factors. There are several methods for estimating reserves, but the choice of which method is to be applied is often based on the data available per time. However, these methods are known to be associated with varying degrees of uncertainties arising from quality of data, the assumptions adopted and the experience of the evaluator. The biggest uncertainty in reserve estimation lies in the inability of the commonly available methods to estimate and discount the huge volumes lost due to unauthorized production by third parties, through crude oil theft, illegal bunkering activities, and spills. This leads to the gross overestimation of reserves and the economic viability of an asset, especially in onshore and shallow offshore assets where such illegal activities are typical and rampant. This paper showcases an approach of estimating reserves, through the integration of multidisciplinary data, which enables the estimation and discounting of crude oil volumes lost due to illegal production from a reservoir.

Comparison of Coal Reserve Estimation Methods, Case Study PT. Bukit Asam Area, South Sumatra, Indonesia

The calculation of coal reserves is influenced by the dimensions or size of the coal deposit. There are several types of coal reserve calculation methods, and the use of these methods is adjusted to existing geological conditions. Each method will produce a different amount of coal reserves, although the location is the same. Besides, the amount of coal mining that can be produced is primarily determined by the mine design, especially the optimal slope as a basis for mining pits in the coal extraction. This research aims to estimate coal reserves based on existing pit designs using a variety of methods. Data on coal thickness and topography are used as the basis for reserves estimation. Coal reserve estimation is conducted in several methods: nearest neighbor point (NNP), inverse distance weighted (IDW), and kriging using Surfer 13 software. The results of the reserves estimation indicate that kriging is the best method by providing the smallest error value with an RMSE value of 0.67 and coal reserves of 27,801,543 tons.

Reservoir characterization and identification of new prospect in Srikail gas field using wireline and seismic data

Although reservoir characterization has been carried out by many researchers on the sedimentary package of the Bengal basin hydrocarbon province, integration of petrophysical and seismic sequence-based reservoir evaluation is rarely taken into account. This paper focuses on the identification of gas zones, reserve estimation and identification of new prospects in Srikail gas field within the eastern fold belt of Bengal basin integrating four wireline logs and 2D seismic data. Our study finds seven hydrocarbon-bearing zones (A, B, C, D, E, F and G) within the measured depth between 2429.5 and 3501 m. Petrophysical properties of seven hydrocarbon-bearing zones indicate that they are good quality reservoir sands. The gas horizons were mapped on seismic sections which reveal that the NW–SE anticlinal structure is largely affected by channels in the crest and western flank. The channels are infilled by fine-grained sediments which act as cap rock on northern and western parts of the structure. Thus, the anticlinal structure and fine-grained sediments make a potential trap for hydrocarbon accumulation and laterally and vertically well-distributed sequence remnants are the main reservoir rocks in this area. Volumetric reserve estimation of these sands provided a total gas initially in place as 552 billion cubic feet. Moreover, all the four wells are drilled in the southern block of the structure, and since there is a structural continuity from south to the north, it is highly recommended to drill a well up to 3000 m depth in the northern block to test its hydrocarbon potentiality. Overall, the outcomes of this study contribute new insights for reservoir characterization and identification of new prospects in an efficient way.

Intelligent Prediction for Rock Porosity While Drilling Complex Lithology in Real Time

Rock porosity is an important parameter for the formation evaluation, reservoir modeling, and petroleum reserve estimation. The conventional methods for determining the rock porosity are considered costly and time-consuming operations during the well drilling. This paper aims to predict the rock porosity in real time while drilling complex lithology using machine learning. In this paper, two intelligent models were developed utilizing the random forest (RF) and decision tree (DT) techniques. The drilling parameters include weight on bit, torque, standpipe pressure, drill string rotation speed, rate of penetration, and pump rate. Two datasets were employed for building the models (3767 data points) and for validating the developed models (1676 data points). Both collected datasets have complex lithology of carbonate, sandstone, and shale. Sensitivity and optimization on different parameters for each technique were conducted to ensure optimum prediction. The models’ performance was checked by four performance indices which are coefficient of determination (R2), average absolute percentage error (AAPE), variance account for (VAF), and a20 index. The results indicated the strong porosity prediction capability for the two models. DT model showed R2 of 0.94 and 0.87 between the predicted and actual porosity values with AAPE of 6.07 and 9% for training and testing, respectively. Generally, RF provided a higher level of strong prediction than DT as RF achieved R2 of 0.99 and 0.90 with AAPE of 1.5 and 7% for training and testing, respectively. The models’ validation proved a high prediction performance as DT achieved R2 of 0.88 and AAPE of 8.58%, while RF has R2 of 0.92 and AAPE of 6.5%.