A method of analysing seismic data to detect possible hydrocarbons includes determining a set of data tiles from a seismic data cube of seismic data and testing each data tile in the set of data tiles to determine whether it corresponds to a possible fluid contact.

Various embodiments described herein provide methods of hydrocarbon management and associated systems and/or computer readable media including executable instructions. Such methods (and by extension their associated systems and/or computer readable media for implementing such methods) may include obtaining geophysical data (e.g., seismic or other geophysical data) from a prospective subsurface formation (that is, a potential formation or other subsurface region of interest for any of various reasons, but in particular due to potential for production of hydrocarbons) and using a trained machine learning (ML) system for direct hydrocarbon indicators (DHI) analysis of the obtained geophysical data. Hydrocarbon management decisions may be guided by the DHI analysis.

It discloses an experimental apparatus and experimental method for physical modeling of a fluid migration and accumulation process with contemporaneous structural deformation. The experimental apparatus comprises a structural deformation experiment box, a structural deformation control device, a fluid control device and an experimental control device; the structural deformation experiment box is installed in a basket experiment module on a cantilever of a drum centrifuge, the structural deformation control device can extend and compress an experimental model in horizontal and vertical directions; and fluid cylinders of the fluid control device can be filled with fluids or plastic materials.

Wavelet estimation may be performed in a reservoir simulation model that is constrained by seismic inversion data and well logs. A synthetic seismic trace is generated along with an estimated wavelet. The reservoir simulation model is revised based on results from model comparisons to actual data or base seismic data and is then used to perform a wavelet estimation. The estimated wavelet may then be used to plan further production at the well site environment, additional production at additional well site environments or any other production and drilling operation for any given present or future well site environment.

A method of identifying regions in a subsurface that may be a hydrocarbon reservoir, the method including: extracting features from cross-attribute clusters; assigning a distance metric and linkage criterion in feature space; calculating, with a computer, a degree of anomaly for the cross-attribute clusters in the feature space; ranking the cross-attribute clusters in accordance with the degree of anomaly; and prospecting for hydrocarbons by investigating a subsurface region in accordance with the rankings.

Various embodiments described herein provide methods of hydrocarbon management and associated systems and/or computer readable media including executable instructions. Such methods (and by extension their associated systems and/or computer readable media for implementing such methods) may include obtaining geophysical data (e.g., seismic or other geophysical data) from a prospective subsurface formation (that is, a potential formation or other subsurface region of interest for any of various reasons, but in particular due to potential for production of hydrocarbons) and using a trained machine learning (ML) system for direct hydrocarbon indicators (DHI) analysis of the obtained geophysical data. Hydrocarbon management decisions may be guided by the DHI analysis.

Various embodiments described herein provide methods of hydrocarbon management and associated systems and/or computer readable media including executable instructions. Such methods (and by extension their associated systems and/or computer readable media for implementing such methods) may include obtaining geophysical data (e.g., seismic or other geophysical data) from a prospective subsurface formation (that is, a potential formation or other subsurface region of interest for any of various reasons, but in particular due to potential for production of hydrocarbons) and using a trained machine learning (ML) system for direct hydrocarbon indicators (DHI) analysis of the obtained geophysical data. Hydrocarbon management decisions may be guided by the DHI analysis.

Systems and methods for simulating an effect of multiple seismic sources concurrently on a geologic formation are provided. Data is read from a seismic source file that describes at least two seismic source types. The concurrent propagation of acoustic energy from the at least two seismic sources types through the geologic formation is modeled. A seismic output file is then generated.

Methods, apparatuses, and computer-readable media are set forth for visualizing and interacting with well production data in a three-dimensional or four-dimensional environment, e.g., using a volumetric well production display representation representing a well in an oilfield and including a plurality of display characteristics configured to display historical production data for the well over a time period.

In some embodiments, a system, as well as a method and an article, may operate to generate a first matrix, based on equations that model a reservoir, that includes mass conservation and volume balance information for grid blocks in the reservoir; to generate a second matrix, based on the first matrix, that includes saturation information and pressure information of each grid block; to remove the saturation information from the second matrix to generate a third matrix that includes only pressure information; to solve the third matrix to generate a first pressure solution; to solve the second matrix based on the first pressure solution to generate a first saturation solution and a second pressure solution; and to use the first saturation solution and the second pressure solution to generate a solution of the first matrix. Additional apparatus, systems, and methods are disclosed.