Landmark Graphics Corporation

Jin Fei

Jeffrey Marc Yarus

Richard L Chambers

Robello Samuel

Genbao Shi

Zhengchun Liu

Rae Mohan Srivastava

Ashwani Dev

Yogendra Narayan Pandey

Trace Boone Smith

Shaolong Wu

Yevgeniy Zagayevskiy

Zhenchun Liu

Systems and methods for modeling petroleum reservoir properties using a gridless reservoir simulation model are provided. Data relating to geological properties of a reservoir formation is analyzed. A tiered hierarchy of geological elements within the reservoir formation is generated at different geological scales, based on the analysis. The geological elements at each of the different geological scales in the tiered hierarchy are categorized. Spatial boundaries between the categorized geological elements are defined for each of the geological scales in the tiered hierarchy. A scalable and updateable gridless model of the reservoir formation is generated, based on the spatial boundaries defined for at least one of the geological scales in the tiered hierarchy.

Point-vector based modeling of petroleum reservoir properties for a gridless reservoir simulation model

A method may comprise: modeling a complex fracture network within the subterranean formation with a mathematical model based on a natural fracture network map and measured data of the subterranean formation collected in association with a fracturing treatment of the subterranean formation to produce a complex fracture network map; importing microseismic data collected in association with the fracturing treatment of the subterranean formation into the mathematical model; identifying directions of continuity in the microseismic data via a geostatistical analysis that is part of the mathematical model; and correlating the directions of continuity in the microseismic data to the complex fracture network with the mathematical model to produce a microseismic-weighted (MSW) complex fracture network map.

Geostatistical analysis of microseismic data in fracture modeling

The selection of a candidate formation realization(s) from a plurality of formation realizations may be done with a classification and regression tree (CART) analysis taking into account petrophysical and geological properties. For example, a method may include applying a CART analysis to a plurality of formation realizations using a first formation property as a predictor in the CART analysis, wherein the plurality of formation realizations are for a second formation property and are based on at least one measured formation property, thereby yielding an association between the first and second properties for each of the plurality of formation realizations; analyzing a strength of the association for each of the plurality of formation realizations; and selecting a candidate formation realization from the plurality of formation realizations based on the strength of the association. The identified candidate formation realization(s) may then be used to develop the parameters of subsequent wellbore operations.

Classification and regression tree analysis of formation realizations

A method may include drilling a deviated wellbore penetrating a subterranean formation according to bottom hole assembly parameters and surface parameters; collecting real-time formation data during drilling; updating a model of the subterranean formation based on the real-time formation data and deriving formation properties therefrom; collecting survey data corresponding to a location of a drill bit in the subterranean formation; deriving a target well path for the drilling based on the model of the subterranean formation; deriving a series of trajectory well paths based on the formation properties, the survey data, the bottom hole assembly parameters, and the surface parameters and uncertainties associated therewith; deriving an actual well path based on the series of trajectory well paths; deriving a deviation between the target well path and the actual well path; and adjusting the bottom hole assembly parameters and the surface parameters to maintain the deviation below a threshold.

Real-time trajectory control during drilling operations

A computing device facilitates the organization of a plurality of three-dimensional geological data realizations into respective one-dimensional arrays of geological property values, with each geological property value corresponding to a three-dimensional grid location of a respective three-dimensional geological data realization. The computing device then facilitates the grouping of the one-dimensional arrays into two or more array clusters based on a comparison of geometric locations of the geological property values within the respective arrays, and selects at least one of the plurality of three-dimensional geological data realizations for each of the two or more array clusters. The selected data realizations are then provided at a user interface.

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Houston, TX, United States of America

Jin Fei