Predicting true sand resistivity in laminated shaly sands with artificial intelligence

A computer-implemented method, comprising:

• obtaining well data of a target well, the well data comprising well logs of multiple types of the target well;
• determining one or more reservoir parameters of the target well based on one or more of the well logs of the multiple types in the well data;
• and
• predicting a specific well log of the target well by a trained artificial intelligence (AI) model with the well logs of the multiple types of the target well and the one or more reservoir parameters of the target well as inputs of the AI model, the AI model being trained using well logs of the multiple types of existing wells and the one or more reservoir parameters of the existing wells as training inputs and known specific well logs for the existing wells as a training output.

A computing system comprising:

• at least one processor;
• and
• at least one non-transitory machine readable storage medium coupled to the at least one processor having machine-executable instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
• obtaining basic log data of a target well, the basic log data comprising well logs of multiple types of the target well, the multiple types comprising two or more of resistivity, density, neutron, and gamma ray;
• and
• predicting a true sand resistivity (RSS) log of the target well using a trained artificial intelligence (AI) model with inputs comprising the well logs of the multiple types of the target well, the AI model being trained with well logs of the multiple types of existing wells as training inputs and known RSS logs of the existing wells as a training output.