All Outputs (21)

A Bayesian multi-region radial composite reservoir model for deconvolution in well test analysis (2022)
Journal Article
Botsas, T., Cumming, J., & Jermyn, I. (2022). A Bayesian multi-region radial composite reservoir model for deconvolution in well test analysis. Journal of the Royal Statistical Society: Series C, 71(4), 951-968. https://doi.org/10.1111/rssc.12562

In petroleum well test analysis, deconvolution is used to obtain information about the reservoir system. This information is contained in the response function, which can be estimated by solving an inverse problem in the pressure and flow rate measur... Read More about A Bayesian multi-region radial composite reservoir model for deconvolution in well test analysis.

Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches (2021)
Book Chapter
Errington, A., Einbeck, J., & Cumming, J. (2021). Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches. In M. Vasile, & D. Quagliarella (Eds.), Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications (393-405). Springer Verlag. https://doi.org/10.1007/978-3-030-80542-5_24

If individuals are exposed to ionising radiation, due to some radiation accident, for medical reasons, or during spaceflight, there is often a need to estimate the contracted radiation dose. The field of biodosimetry is concerned with estimating the... Read More about Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches.

The effect of data aggregation on dispersion estimates in count data models (2021)
Journal Article
Errington, A., Einbeck, J., Cumming, J., Rössler, U., & Endesfelder, D. (2022). The effect of data aggregation on dispersion estimates in count data models. International Journal of Biostatistics, 18(1), 183-202. https://doi.org/10.1515/ijb-2020-0079

For the modelling of count data, aggregation of the raw data over certain subgroups or predictor configurations is common practice. This is, for instance, the case for count data biomarkers of radiation exposure. Under the Poisson law, count data can... Read More about The effect of data aggregation on dispersion estimates in count data models.

Bayes Linear Emulation of Simulated Crop Yield (2021)
Book Chapter
Hasan, M. M., & Cumming, J. A. (2021). Bayes Linear Emulation of Simulated Crop Yield. In Y. P. Chaubey, S. Lahmiri, F. Nebebe, & A. Sen (Eds.), Applied Statistics and Data Science:Proceedings of Statistics 2021 Canada, Selected Contributions (145-151). Springer Verlag. https://doi.org/10.1007/978-3-030-86133-9_7

The analysis of the output from a large-scale computer simulation experiment can pose a challenging problem in terms of size and computation. We consider output in the form of simulated crop yields from the Environmental Policy Integrated Climate (EP... Read More about Bayes Linear Emulation of Simulated Crop Yield.

A Bayesian non-linear hierarchical framework for crop models based on big data outputs (2020)
Presentation / Conference
Hasan, M. M., & Cumming, J. (2020, December). A Bayesian non-linear hierarchical framework for crop models based on big data outputs. Paper presented at 13th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2020), King's College London, England

Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach (2020)
Conference Proceeding
Cumming, J., Botsas, T., Jermyn, I., & Gringarten, A. (2020). Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach. In SPE Virtual Europec 2020 ; proceedings (SPE-200617-MS). https://doi.org/10.2118/200617-ms

Objectives/Scope: A stable, single-well deconvolution algorithm has been introduced for well test analysis in the early 2000’s, that allows to obtain information about the reservoir system not always available from individual flow periods, for exampl... Read More about Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach.

Statistical Approach to Raman Analysis of Graphene-Related Materials: Implications for Quality Control (2020)
Journal Article
Goldie, S. J., Bush, S., Cumming, J. A., & Coleman, K. S. (2020). Statistical Approach to Raman Analysis of Graphene-Related Materials: Implications for Quality Control. ACS Applied Nano Material, 3(11), 11229-11239. https://doi.org/10.1021/acsanm.0c02361

A statistical method to determine the number of measurements required from nanomaterials to ensure reliable and robust analysis is described. Commercial products utilizing graphene are in their infancy and recent investigations of commercial graphene... Read More about Statistical Approach to Raman Analysis of Graphene-Related Materials: Implications for Quality Control.

Using Deconvolution to Estimate Unknown Well Production from Scarce Wellhead Pressure Data (2020)
Conference Proceeding
Aluko, L., Cumming, J., & Gringarten, A. (in press). Using Deconvolution to Estimate Unknown Well Production from Scarce Wellhead Pressure Data. . https://doi.org/10.2118/201667-ms

Constrained Least-Squares Multiwell Deconvolution (2019)
Conference Proceeding
Cumming, J., Jaffrezic, V., Whittle, T., & Gringarten, A. (2019). Constrained Least-Squares Multiwell Deconvolution. In Proceedings of the SPE Western Regional Meeting 2019. https://doi.org/10.2118/195271-ms

In this paper, we reduce non-uniqueness and ensure physically feasible results in multiwell deconvolution by incorporating constraints and knowledge to the methodology of Cumming et al. (2014). The constraints discourage non-physical shapes for the d... Read More about Constrained Least-Squares Multiwell Deconvolution.

Field Applications of Constrained Multiwell Deconvolution (2019)
Conference Proceeding
Jaffrezic, V., Razminia, K., Cumming, J., & Gringarten, A. (in press). Field Applications of Constrained Multiwell Deconvolution. . https://doi.org/10.2118/195516-ms

Known Boundary Emulation of Complex Computer Models (2019)
Journal Article
Vernon, I., Jackson, S., & Cumming, J. (2019). Known Boundary Emulation of Complex Computer Models. SIAM/ASA Journal on Uncertainty Quantification, 7(3), 838-876. https://doi.org/10.1137/18m1164457

Computer models are now widely used across a range of scientific disciplines to describe various complex physical systems, however to perform full uncertainty quantification we often need to employ emulators. An emulator is a fast statistical constru... Read More about Known Boundary Emulation of Complex Computer Models.

Multiwell Deconvolution for Shale Gas (2016)
Conference Proceeding
Tung, Y., Virues, C., Cumming, J., & Gringarten, A. (2016). Multiwell Deconvolution for Shale Gas. . https://doi.org/10.2118/180158-ms

In the last decade, single well deconvolution (von Schroeter et al., 2001) has become recognised as a powerful tool for reservoir characterization. Deconvolution transforms well test pressure data measured at varying rates into an equivalent unit rat... Read More about Multiwell Deconvolution for Shale Gas.

Understanding the accuracy of pre-symptomatic diagnosis of sepsis (2016)
Report
Cumming, J., Riseth, A., & Williams, J. (2016). Understanding the accuracy of pre-symptomatic diagnosis of sepsis. [No known commissioning body]

Research is currently being undertaken to expand the window of efficiency for medical treatment through pre-symptomatic diagnosis. This is achieved through an observational clinical study. Blood is taken from consenting elective surgery patients from... Read More about Understanding the accuracy of pre-symptomatic diagnosis of sepsis.

Application of Multiple Well Deconvolution Method in a North Sea Field (2015)
Conference Proceeding
Thornton, E., Mazloom, J., Gringarten, A., & Cumming, J. (2015). Application of Multiple Well Deconvolution Method in a North Sea Field. . https://doi.org/10.2118/174353-ms

Multiwell Deconvolution (2014)
Journal Article
Cumming, J., Wooff, D., Whittle, T., & Gringarten, A. (2014). Multiwell Deconvolution. SPE Reservoir Evaluation & Engineering, 17(04), 457-465. https://doi.org/10.2118/166458-pa

In well-test analysis, deconvolution is used to transform variablerate-pressure data into a single constant-rate drawdown suitable for interpretation. It is becoming part of a standard workflow for exploration and appraisal well-test analyses and in... Read More about Multiwell Deconvolution.

Multiple Well Deconvolution (2013)
Conference Proceeding
Cumming, J., Wooff, D., Whittle, T., & Gringarten, A. (2013). Multiple Well Deconvolution. . https://doi.org/10.2118/166458-ms

Assessing the Non-Uniqueness of the Well Test Interpretation Model Using Deconvolution (2013)
Conference Proceeding
Cumming, J., Wooff, D., Whittle, T., Crossman, R., & Gringarten, A. (2013). Assessing the Non-Uniqueness of the Well Test Interpretation Model Using Deconvolution. . https://doi.org/10.2118/164870-ms

Bayes linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments (2010)
Book Chapter
Cumming, J., & Goldstein, M. (2010). Bayes linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments. In A. O'Hagan, & M. West (Eds.), The Oxford handbook of applied Bayesian analysis (241-270). Oxford University Press

Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations (2009)
Journal Article
Cumming, J., & Goldstein, M. (2009). Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations. Technometrics, 51(4), 377-388. https://doi.org/10.1198/tech.2009.08015

We consider the problem of designing for complex high-dimensional computer models that can be evaluated at different levels of accuracy. Ordinarily, this requires performing many expensive evaluations of the most accurate version of the computer mode... Read More about Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations.

Dimension reduction via principal variables (2007)
Journal Article
Cumming, J., & Wooff, D. (2007). Dimension reduction via principal variables. Computational Statistics & Data Analysis, 52(1), 550-565. https://doi.org/10.1016/j.csda.2007.02.012

For many large-scale datasets it is necessary to reduce dimensionality to the point where further exploration and analysis can take place. Principal variables are a subset of the original variables and preserve, to some extent, the structure and info... Read More about Dimension reduction via principal variables.