Partially coupling a geomechanical simulation with a reservoir simulation facilitates predicting strain behavior for a reservoir from production and injection processes. A method comprises generating a geomechanical model based on a mechanical earth model that represents a subsurface area. The geomechanical model indicates a division of the mechanical earth model into a plurality of grid cells that each correspond to a different volume of the subsurface area. Based on a first virtual compaction experiment with the geomechanical model, compaction curves are generated. The compaction curves represent porosity as a function of stress. The compaction curves are converted from porosity as a function of stress to porosity as a function of pore pressure. The geomechanical model is partially coupled to a reservoir simulation model using the converted compaction curves.

Methods and an alarming system for long-term carbon dioxide sequestration in a geologic reservoir are described. The geologic reservoir may be a water filled sandstone reservoir or a carbonate reservoir. A reservoir model is constructed to show the effects of varying injection pressures, the number of injection wells, the arrangement of injection wells, the boundary conditions and sizes of the reservoir on caprock uplift, fracture formation and fracture reactivation. The alarming system generates an alarm when caprock uplift that surpasses a threshold is detected. The injection pressures and the number of injection wells operating may be varied in response to the alarm.

Systems and methods relate to generating a self-consistent sediment model. Initially, void ratio extrema are determined for each sediment layer in a sediment column based on historical data or a direct measurement of the sediment column. Initial stress is determined for each sediment layer based on the void ratio extrema. A porosity model is generated for each sediment layer based on the void ratio extrema and the initial stress. At this stage, measured data is obtained for each sediment layer from a data collection device positioned at or near a geographic location of the sediment column. The porosity model of each of the sediment layer is combined with the measured data of the sediment layer to generate the self-consistent sediment model for each sediment layer. The porosity model and the self-consistent sediment model of each sediment layer is updated based on updated measured data obtained from the data collection device.

A system for estimating a property of a region of interest includes an acoustic measurement device including a transmitter configured to emit an acoustic signal having at least one selected frequency configured to penetrate a surface of a borehole in an earth formation and produce internal diffuse backscatter from earth formation material behind the surface and within the region of interest, and a receiver configured to detect return signals from the region of interest and generate return signal data. The system also includes a processing device configured to receive the return signal data, process the return signal data to identify internal diffuse backscatter data indicative of the internal diffuse backscatter, calculate one or more characteristics of the internal diffuse backscatter, and estimate a property of the region of interest based on the one or more characteristics of the internal diffuse backscatter.

Systems and methods include a computer-implemented method for optimizing variogram ranges uncertainties. Variogram modeling is performed using variogram models on wells in parallel (major), normal (minor), and vertical directions for continuous log porosity to select a best-fit variogram model using large uncertainty ranges and a preferred-normal distribution. A distribution of geological properties is determined onto the best-fit variogram model. Multiple realizations are executed to determine predicted porosities over the best-fit variogram model. Correlation coefficients of actual porosity versus predicted porosity are generated using the multiple realizations. The process is repeated until a correlation meets a predetermined acceptance criteria. A variogram range for the best-fit variogram model is optimized using a high correlation realization. Correlations are determined for the subset of wells by executing multiple realizations using a same seed number. Final optimized variogram ranges uncertainties are determined by repeating the optimizing and determining until an acceptance correlation is achieved.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to generate generating geo-cellular models with improved lacunae. In one aspect, a method includes receiving a seismic dataset of a surveyed subsurface, and the seismic dataset includes seismic porosities in depth of the surveyed subsurface. Seismic porosities resampled into a three dimensional (3D) geological fine layer model grid. Seismic porosities at well locations are extracted using the 3D geological fine layer model grid. Log porosities and the seismic porosities are upscaled into coarse layers, and the coarse layers are identical for all the well locations. Match factors are determined based on differences between the upscaled log porosities and the downscaled seismic porosities. Co-krig the log porosities are correlated with the 3D geological fine layer model grid using the match factors as a soft constraint to produce a final 3D model.

Identifying a low permeable conglomerate diagenetic trap can be implemented according to a method that comprises: determining a first relation curve between a depth and a critical physical property of a known diagenetic trap in a target work area, and a second relation curve between a reservoir physical property of the known diagenetic trap and a designated seismic attribute; determining a third relation curve between the depth and the critical physical property in the target work area and the designated seismic attribute according to the first relation curve and the second relation curve; and performing a diagenetic trap identification of the target work area according to the third relation curve. Identification accuracy of a low permeable conglomerate diagenetic trap can thereby be improved.

Provided are techniques for developing a hydrocarbon reservoir that include: determining a reservoir model of a hydrocarbon reservoir that includes columns of gridblocks that represent a vertical segment of the reservoir; acquiring nano-images of a rock sample of the reservoir; determining, based on the nano-images, properties of an inorganic pore network and an organic pore network of the rock sample; generating a divided reservoir model of the reservoir that represents the inorganic and organic pore networks of the reservoir, including: for each of the columns of gridblocks, dividing each of the gridblocks of the column into: a water-wet gridblock associated with the properties of the inorganic pore network determined based on the nano-images; and an oil-wet gridblock associated with the properties of the organic pore network determined based on the nano-images; and generating, using the divided reservoir model, a simulation of the hydrocarbon reservoir.

Systems and methods include a method for generating a real-time reservoir porosity log. Historical gas-porosity data is received from previously-drilled and logged wells. The historical gas-porosity data identifies relationships between gas measurements obtained during drilling and reservoir porosity determined after drilling. A gas-porosity model is trained using machine learning and the historical gas-porosity data. Real-time gas measurements are obtained during drilling of a new well. A real-time reservoir porosity log is generated for the new well using the gas-porosity model and real-time gas measurements.

Methods may include calculating a formation permeability for a subterranean formation from a combination of dielectric measurements and acoustic measurements, wherein the formation permeability is calculated according to the formula: k.sub.g=a(V.sub.xσ.sub.w/ε.sub.r).sup.b, where V.sub.x is either V.sub.p, V.sub.s, or V.sub.p/V.sub.s, σ is formation conductivity, Ø.sub.w is water-filled porosity, and a and b are constants that are empirically determined for the frequency selected with respect to V.sub.x; and creating a design for a wellbore operation from the calculated formation permeability. Methods may also include obtaining a dielectric measurement from a downhole formation; obtaining an acoustic measurement from a downhole formation; and calculating a formation permeability from a combination of the dielectric measurement and the acoustic measurement.