A computer-implemented method for optimizing and automating a well-planning process using an integrated multi-dimensional geological model, the method comprising: identifying an area of interest in a subterranean region of a geological area; obtaining , by the integrated multi-dimensional geological model, seismic data generated from sampling sensors deployed in the subterranean region, wherein the data describes characteristics of an underground formation in the subterranean region of the geological area; modeling , by the integrated multi-dimensional geological model, properties of reservoirs in the underground formation based on the characteristics described in the seismic data; in response to modeling properties of the reservoir, generating , by the integrated multi-dimensional geological model, a risk map configured to estimate a probability of contacting a particular area of the reservoir that has properties corresponding to at least one of the modeled properties, wherein generating the risk map comprises performing probabilistic modeling using a predictive risk model of the integrated multi-dimensional geological model, wherein performing probabilistic modeling using the predictive risk model comprises generating the predictive risk model by a machine learning engine based on one or more adaptive analytical rules generated based on the seismic data; computing , by the integrated multi-dimensional geological model and based on the risk map, estimates of hydrocarbon reserves at the particular area of the reservoir; and based on the computed estimates of hydrocarbon reserves, generating , using the integrated multi-dimensional geological model, a well plan that includes position data for drilling a well bore to encounter the hydrocarbon reserves at the particular area of the reservoir, wherein the one or more adaptive analytical rules are iteratively updated based on feedback data comprising seismic data generated at a location selected for drilling from the generated well plan. identifying an area of interest in a subterranean region of a geological area; obtaining , by the integrated multi-dimensional geological model, seismic data generated from sampling sensors deployed in the subterranean region, wherein the data describes characteristics of an underground formation in the subterranean region of the geological area; modeling , by the integrated multi-dimensional geological model, properties of reservoirs in the underground formation based on the characteristics described in the seismic data; in response to modeling properties of the reservoir, generating , by the integrated multi-dimensional geological model, a risk map configured to estimate a probability of contacting a particular area of the reservoir that has properties corresponding to at least one of the modeled properties, wherein generating the risk map comprises performing probabilistic modeling using a predictive risk model of the integrated multi-dimensional geological model, wherein performing probabilistic modeling using the predictive risk model comprises generating the predictive risk model by a machine learning engine based on one or more adaptive analytical rules generated based on the seismic data; computing , by the integrated multi-dimensional geological model and based on the risk map, estimates of hydrocarbon reserves at the particular area of the reservoir; and based on the computed estimates of hydrocarbon reserves, generating , using the integrated multi-dimensional geological model, a well plan that includes position data for drilling a well bore to encounter the hydrocarbon reserves at the particular area of the reservoir, wherein the one or more adaptive analytical rules are iteratively updated based on feedback data comprising seismic data generated at a location selected for drilling from the generated well plan. <EOS>