Quantico Energy Solutions LLC

Quantic Energy Solutions Llo

Barry Fen Zhang

Tuna Altay Sansal

Orlando De Jesus

Edward Tian

Dingding Chen

Bruce H Storm, Jr

Muhlis Unaldi

Jr Bruce H Storm

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.

Earth modeling methods using machine learning

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.

Confidence volumes for earth modeling using machine learning

The present disclosure generally relates to a real-time synthetic logging method for optimizing one or more operations in a well. The method generally includes receiving measurements of one or more parameters in real time while performing operations in the well, the measurements being captured without using tools that include active nuclear sources. The method further includes providing the measurements as input to a machine learning algorithm (MLA) that is trained using historical or training well data. The method further includes generating, using the MLA and based on the measurements, a synthetic mechanical property log of the well. The method further includes generating, based on the synthetic mechanical property log, optimized parameters for at least one operation selected from the following list: drilling the well in real-time; steering the well in real-time; and stimulating a reservoir in real-time.

Real-time synthetic logging for optimization of drilling, steering, and stimulation

Growing community of inventors

Houston, TX, United States of America

Barry Fen Zhang