Abstract: The present disclosure generally relates to synthesizing one or more properties for optimizing one or more operations in a well. Embodiments include receiving measurements or qualitative indicators of one or more parameters in association with performing operations in the well. Embodiments include providing, based on the measurements, one or more inputs to a machine learning algorithm (MLA) that has been trained using historical or training well data. Embodiments include determining, based on one or more outputs from the MLA, one or more synthesized properties relating to the well, wherein the one or more synthesized properties comprise a synthesized pore-pressure at, near, or ahead of a bit position. Embodiments include determining, based on the one or more synthesized properties, one or more optimized parameters relating to at least one of: drilling the well; steering the well; or stimulating a reservoir.

Abstract: Aspects of the present disclosure relate to earth modeling using machine learning. A method includes receiving detected data at a first depth point along a wellbore, providing at least a first subset of the detected data as first input values to a machine learning model, and receiving first output values from the machine learning model based on the first input values. The method includes receiving additional detected data at a second depth point along the wellbore, providing at least a second subset of the additional detected data as second input values to the machine learning model, and receiving second output values from the machine learning model based on the second input values. The method includes combining the first output values at the first depth point and the second output values at the second depth point to generate an updated model of the wellbore, the updated model comprising an earth model.

Abstract: Aspects of the present disclosure relate to confidence volumes for earth modeling using machine learning. A method includes receiving detected data, wherein the detected data includes formation attributes relating to one or more depth points along or near a wellbore. The method further includes providing inputs to a plurality of machine learning models based on the detected data. The method further includes receiving output values from the plurality of machine learning models based on the inputs. The method further includes determining a measure of variance among the output values. The method further includes generating a confidence indicator related to the output values based on the measure of variance.

Abstract: Aspects of the present disclosure relate to confidence volumes for earth modeling using machine learning. A method includes receiving detected data, wherein the detected data includes formation attributes relating to one or more depth points along or near a wellbore. The method further includes providing inputs to a plurality of machine learning models based on the detected data. The method further includes receiving output values from the plurality of machine learning models based on the inputs. The method further includes determining a measure of variance among the output values. The method further includes generating a confidence indicator related to the output values based on the measure of variance.

Abstract: Aspects of the present disclosure relate to earth modeling using machine learning. A method includes receiving detected data at a first depth point along a wellbore, providing at least a first subset of the detected data as first input values to a machine learning model, and receiving first output values from the machine learning model based on the first input values. The method includes receiving additional detected data at a second depth point along the wellbore, providing at least a second subset of the additional detected data as second input values to the machine learning model, and receiving second output values from the machine learning model based on the second input values. The method includes combining the first output values at the first depth point and the second output values at the second depth point to generate an updated model of the wellbore, the updated model comprising an earth model.

Abstract: Aspects of the present disclosure relate to using artificial intelligence for high-resolution earth modeling. Embodiments include receiving training data, comprising: wellbore attributes relating to a plurality of depth points; and adjacent waveform data relating to a first plurality of directions with respect to each depth point of the plurality of depth points. Embodiments include providing at least a subset of the training data as inputs to a machine learning model. Embodiments include receiving outputs from the machine learning model based on the inputs. Embodiments include iteratively adjusting parameters of the machine learning model based on the outputs and the training data.