A data-driven approach to estimating post-discovery parameters of unexplored oilfields

Consider a typical situation where an investor is considering acquiring an unexplored oilfield.The oilfield has undergone a preliminary geological and geophysical study in which pre-discovery data such as lithology,depth,depositional system,diagenetic overprint,structural compartmentalization,and trap type are available.In this situation,investors usually estimate production rates using a volumetric approach.A more accurate estimation of production rates can be obtained using analytical methods,which require additional data such as net pay,porosity,oil formation volume factor,permeability,viscosity,and pressure.We call these data post-discovery parameters because they are only available after discovery through exploration drilling.A data-driven approach to estimating post-discovery parameters of an unexplored oilfield is developed based on its pre-discovery data by learning from proven reservoir data.Using the Gaussian mixture model,and a data-driven reservoir typology based on the joint probability distribution of post-discovery parameters is established.We came up with 12 reservoir types.Subsequently,an artificial neural network classification model with the resilient backpropagation algorithm is used to find relationships between pre-discovery data and reservoir types.Based on k-fold crossvalidation with k?10,the accuracy of the classification model is stable with an average of 87.9%.With our approach,an investor considering acquiring an unexplored oilfield can classify the oilfield's reservoir into a particular type and estimate its post-discovery parameters'joint probability distribution.The investor can incorporate this information into a valuation model to calculate the production rates more accurately,estimate the oilfield's value and risk,and make an informed acquisition decision accordingly.