Data assimilation of sensor measurements to improve production forecast in resource extraction

One of the main challenges in the mining industry is to ensure that short-term production targets are continuously met. The executed mine plan, formulated in an attempt to comply with the targets, often turns out to be suboptimal due to unexpected deviations in the resource model. Recent sensor technology enable the on-line characterization of these deviations at different points along the production process. There exists a potential to improve short-term planning if such on-line measurements can be integrated back into the resource model. This contribution introduces a novel approach to real-time resource model updating. An algorithm was developed that specifically deals with the practical problems of the mineral industry. A case study demonstrates the added value.

[1]  J. A. Vargas-Guzmán,et al.  Sequential kriging and cokriging: Two powerful geostatistical approaches , 1999 .

[2]  J. Chilès,et al.  Geostatistics: Modeling Spatial Uncertainty , 1999 .

[3]  Liangping Li,et al.  An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering , 2011 .

[4]  Charles F. Harvey,et al.  Mapping Hydraulic Conductivity: Sequential Conditioning with Measurements of Solute Arrival Time, Hydraulic Head, and Local Conductivity , 1995 .

[5]  Geir Evensen,et al.  Combining geostatistics and Kalman filtering for data assimilation in an estuarine system , 2002 .

[6]  E. G. Vomvoris,et al.  A geostatistical approach to the inverse problem in groundwater modeling (steady state) and one‐dimensional simulations , 1983 .

[7]  Ning Liu,et al.  Inverse Theory for Petroleum Reservoir Characterization and History Matching , 2008 .

[8]  Hermann Wotruba,et al.  Sensor Technologies : impulses for the Raw Materials Industry , 2014 .

[9]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

[10]  Wolfgang Nowak,et al.  Parameter Estimation by Ensemble Kalman Filters with Transformed Data , 2010 .

[11]  Roussos Dimitrakopoulos,et al.  Conditional simulation algorithms for modelling orebody uncertainty in open pit optimisation , 1998 .

[12]  P. Houtekamer,et al.  A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation , 2001 .

[13]  G. Evensen,et al.  Assimilation of Geosat altimeter data for the Agulhas current using the ensemble Kalman filter with , 1996 .

[14]  Roussos G. Dimitrakopoulos,et al.  Implementation of conditional simulation by successive residuals , 2011, Comput. Geosci..

[15]  R. Dimitrakopoulos,et al.  Conditional Simulation of Random Fields by Successive Residuals , 2002 .

[16]  G. Evensen.  Using the Extended Kalman Filter with a Multilayer Quasi-Geostrophic Ocean Model , 1992 .

[17]  J. Benndorf,et al.  Making Use of Online Production Data: Sequential Updating of Mineral Resource Models , 2015, Mathematical Geosciences.

[18]  M. W. Davis,et al.  Production of conditional simulations via the LU triangular decomposition of the covariance matrix , 1987, Mathematical Geology.