Integration of Tracer Test Data to Refine Geostatistical Hydraulic Conductivity Fields Using Sequential Self-Calibration Method

On the basis of local measurements of hydraulic conductivity, geostatistical methods have been found to be useful in heterogeneity characterization of a hydraulic conductivity field on a regional scale. However, the methods are not suited to directly integrate dynamic production data, such as, hydraulic head and solute concentration, into the study of conductivity distribution. These data, which record the flow and transport processes in the medium, are closely related to the spatial distribution of hydraulic conductivity. In this study, a three-dimensional gradient-based inverse method--the sequential self-calibration （SSC） method--is developed to calibrate a hydraulic conductivity field, initially generated by a geostatistical simulation method, conditioned on tracer test results. The SSC method can honor both local hydraulic conductivity measurements and tracer test data. The mismatch between the simulated hydraulic conductivity field and the reference true one, measured by its mean square error （MSE）, is reduced through the SSC conditional study. In comparison with the unconditional results, the SSC conditional study creates the mean breakthrough curve much closer to the reference true curve, and significantly reduces the prediction uncertainty of the solute transport in the observed locations. Further, the reduction of uncertainty is spatially dependent, which indicates that good locations, geological structure, and boundary conditions will affect the efficiency of the SSC study results.

Quality evaluation of glutenite reservoirs of intergranular-secondary dissolution pore type using a quantitative method

Glutenite(coarse-grained clastic)reservoirs of intergranularesecondary dissolution pore type are dominated by residual intergranular pores and secondary dissolution pores,and characterized by low porosity,low permeability,strong heterogeneity,and highly variable physical properties.It is difficult to conduct a quantitative quality assessment of these reservoirs while their primary control factors remain unclear.In this paper,experimental core data and drilling,logging and seismic data are used to assess the effect of sedimentary facies on reservoir quality.Favorable sedimentary facies zones are identified by analyzing the characteristics of glutenite reservoirs,which includes investigating rock components and their effects on reservoir quality.Argillaceous matrix content and rigid particle content are identified as the primary control factors for these reservoirs.Logging curves sensitive to reservoir quality are selected and examined to continuously characterize the physical parameters of the reservoirs.It establishes a calculation model of reservoir assessment parameters through multivariate regression and determines the quantitative assessment parameter Fr.The quality of the glutenite reservoirs is defined using conventional logging curves.This study also predicts the plane distribution of high-quality reservoirs through geostatistical inversion of the reservoir assessment parameters based on conventional wave impedance inversion,thus providing insight and guidance for quantitative assessment and quality prediction of glutenite reservoirs of the intergranular-secondary dissolution pore type.The application of this method to well deployment based on qualitative evaluation of the glutenite reservoirs in oilfields yielded favorable results.

Joint simulation of cross-correlated ore grades and geological domains:an application to mineral resource modeling

Spatial modeling of ore grades is frequently impacted by the local variation in geological domains such as lithological characteristics,rock types,and geological formations.Disregarding this information may lead to biased results in the final ore grade block model,subsequently impacting the downstream processes in a mining chain project.In the current practice of ore body evaluation,which is known as stochastic cascade/hierarchical geostatistical modeling,the geological domain is first characterized,and then,within the geological model,the ore grades of interest are evaluated.This practice may be unrealistic in the case when the variability in ore grade across the boundary is gradual,following a smooth transition.To reproduce such characteristics,the cross dependence that exists between the ore grade and geological formations is considered in the conventional joint simulation between continuous and categorical variables.However,when using this approach,only one ore variable is considered,and its relationship with other ore grades that may be available at the sample location is ignored.In this study,an alternative approach to jointly model two cross-correlated ore grades and one categorical variable(i.e.,geological domains)with soft contact relationships that exist among the geological domains is proposed.The statistical and geostatistical tools are provided for variogram inference,Gibbs sampling,and conditional cosimulation.The algorithm is also tested by applying it to a Cu deposit,where the geological formations are managed by the local and spatial distribution of two cross-correlated ore grades,Cu and Au,throughout the deposit.The results show that the proposed algorithm outperforms other geostatistical techniques in terms of global and local reproduction of statistical parameters.

Research on 3D Distribution of Meandering River Sand Body Using Sedimentary Facies Method and 3D Geological Modeling

Sedimentary facies study is an important method in describing the property and distribution of reservoir. 3D geological modeling is a powerful tool in 3D characterization of geological bodies. By combining the sedimentary facies study with 3D geological modeling to generate 3D sedimentary facies model, the 3D geometry and distribution feature of sand bodies can be more accurately characterized, particularly in 3D view. In Liuchu oilfield of Jizhong depression, the Ed2IV formation was recognized as meandering river deposition facies and five sedimentary facies were identified, which include point bar sand, levee, channel margin, abandoned channel and floodplain. All the 24 sand body facies in Ed2IV were mapped and the 3D sedimentary facies model established based on 2D facies maps. The result shows that the 3D sedimentary facies model is well matched for the research result of sedimentary facies. Being an extension of traditional sedimentary study, the 3D sedimentary facies model can be used to describe the 3D geometry and distribution orders of a single sand body more reliably and more accurately.