Abstract: A method for formation properties prediction in near-real time may include obtaining lab measurements of existing drill cuttings at a plurality of depths of a first well. The method may include obtaining historical drilling surface data at the plurality of depths from a plurality of wells. The method may include obtaining real-time digital photos and real-time drilling surface data of new drill cuttings at a new depth of a new well. The method may include generating, using a prediction model, predicted formation properties of the new drill cuttings based on the real-time digital photos, the real-time drilling surface data, and the new depth. The method may include predicting, using a near-real-time model and the predicted formation properties, near-real-time formation properties in the new well, wherein the prediction model comprises a historical model that employs a machine-learning algorithm.

Abstract: A method for determining a property of a formation, including the steps: drilling a well in the formation, collecting drill cuttings from the well, taking a digital image of each drill cutting, entering each digital image to a trained first model that outputs a predicted lithology class of each drill cutting from each digital image, taking a random number of X-ray diffraction (XRD) images of the drill cuttings, while at least one XRD image is selected from each lithology class, entering each XRD image and the corresponding digital image into a trained second model that predicts a property of the drill cuttings, and determining the property of the formation by determining the properties of the drill cuttings as a function of the depth of the drill cuttings.

Abstract: A method for determining a property of a formation, including the steps: drilling a well in the formation, collecting drill cuttings from the well, taking a digital image of each drill cutting, entering each digital image to a trained first model that outputs a predicted lithology class of each drill cutting from each digital image, taking a random number of X-ray diffraction (XRD) images of the drill cuttings, while at least one XRD image is selected from each lithology class, entering each XRD image and the corresponding digital image into a trained second model that predicts a property of the drill cuttings, and determining the property of the formation by determining the properties of the drill cuttings as a function of the depth of the drill cuttings.

Abstract: A method for formation properties prediction in near-real time. The method may include obtaining lab measurements of existing drill cuttings at a plurality of depths of a first well. The method may include obtaining historical drilling surface data at the plurality of depths from a plurality of wells. The method may include obtaining real-time digital photos and real-time drilling surface data of new drill cuttings at a new depth of a new well. The method may include generating, using a prediction model, predicted formation properties of the new drill cuttings based on the real-time digital photos, the real-time drilling surface data, and the new depth. The method may include predicting, using a near-real-time model and the predicted formation properties, near-real-time formation properties in the new well, wherein the prediction model comprises a historical model that employs a machine-learning algorithm.