Abstract: Systems and methods include a method for predicting geological formation tops. First well log data associated with a key master well is received. Formation data identifying tops of formations confirmed in the key master well is received. Merged key master well and formation data is generated in a dynamic time warping (DTW)-readable format by merging the first well log data with the formation data. Second well log data associated with a training well located in geographic proximity to the key master well is received. The second well log data is formatted into the DTW-readable format. A DTW function is executed to generate indices associated with the formation tops. The DTW function uses the merged key master well and formation data and the formatted second well log data as DTW function inputs. Predicted geological formation tops for the training well are predicted using the generated indexes.

Abstract: Methods and systems, including computer programs encoded on a computer storage medium can be used for an integrated methodology that can be used by a computing system to automate processes for generating, and updating (e.g., in real-time), subsurface reservoir models. The methodology and automated approaches employ technologies relating to machine learning and artificial intelligence (AI) to process seismic data and information relating to seismic facies.

Abstract: Methods and systems, including computer programs encoded on a computer storage medium can be used for an integrated methodology that can be used by a computing system to automate processes for generating, and updating (e.g., in real-time), subsurface reservoir models. The methodology and automated approaches employ technologies relating to machine learning and artificial intelligence (AI) to process seismic data and information relating to seismic facies.

Abstract: Methods and systems, including computer programs encoded on a computer storage medium can be used for an integrated methodology that can be used by a computing system to automate processes for generating, and updating (e.g., in real-time), subsurface reservoir models. The methodology and automated approaches employ technologies relating to machine learning and artificial intelligence (AI) to process seismic data and information relating to seismic facies.

Abstract: Methods and systems, including computer programs encoded on a computer storage medium can be used for an integrated methodology that can be used by a computing system to automate processes for generating, and updating (e.g., in real-time), subsurface reservoir models. The methodology and automated approaches employ technologies relating to machine learning and artificial intelligence (AI) to process seismic data and information relating to seismic facies.

Abstract: Methods and systems, including computer programs encoded on a computer storage medium can be used for an integrated methodology that can be used by a computing system to automate processes for generating, and updating (e.g., in real-time), subsurface reservoir models. The methodology and automated approaches employ technologies relating to machine learning and artificial intelligence (AI) to process seismic data and information relating to seismic facies.

Abstract: Methods and systems, including computer programs encoded on a computer storage medium can be used for an integrated methodology that can be used by a computing system to automate processes for generating, and updating (e.g., in real-time), subsurface reservoir models. The methodology and automated approaches employ technologies relating to machine learning and artificial intelligence (AI) to process seismic data and information relating to seismic facies.

Abstract: Systems and methods include a method for predicting geological formation tops. First well log data associated with a key master well is received. Formation data identifying tops of formations confirmed in the key master well is received. Merged key master well and formation data is generated in a dynamic time warping (DTW)-readable format by merging the first well log data with the formation data. Second well log data associated with a training well located in geographic proximity to the key master well is received. The second well log data is formatted into the DTW-readable format. A DTW function is executed to generate indices associated with the formation tops. The DTW function uses the merged key master well and formation data and the formatted second well log data as DTW function inputs. Predicted geological formation tops for the training well are predicted using the generated indexes.