Saudi Arabian Oil Company

Ammar AlQatari

Murtadha J AlTammar

Mohamed Larbi Zeghlache

Khalid Mohammed Alruwaili

Mustafa Hassan Alaliwat

A method for identifying fractures within a geological formation is provided. The method includes obtaining a plurality of images from within a first well located within the geological formation, wherein at least one image of the plurality of images comprises an identified fracture in the geological formation, obtaining a first mudlogging dataset associated with the plurality of images, correlating one or more helium measurements from the first mudlogging dataset with each image of the plurality of images via a machine learning engine to train a machine learning model to obtained a trained machine learning model, obtaining, by the trained machine learning model, a second mudlogging dataset associated with an interval of a second well, and determining a presence of one or more fractures in the interval of the second well by the machine learning model based on helium content determined from the second mudlogging dataset.

Systems and methods for identifying fractures within a geological formation

A method involves obtaining a current production rate, obtaining a current wellhead pressure, computing a current smoothed production rate from the current production rate, computing a current smoothed wellhead pressure from the current wellhead pressure, predicting based on the current smoothed production rate, the current smoothed wellhead pressure, and the current surface choke size, a future smoothed production rate and a future smoothed wellhead pressure, computing a change of the future vs the current smoothed production rate, and computing a change of the future vs the current smoothed wellhead pressure. The method further involves comparing the change of the future vs the current smoothed production rate against a first pre-specified threshold production rate derivative, comparing the change of the future vs the current smoothed wellhead pressure against a first pre-specified threshold wellhead pressure derivative, and based on an outcome of the comparison, determining whether future sanding is detected.

Method and system for detecting and predicting sanding and sand screen deformation

A system and method map a borehole using LIDAR. The system comprises a tool and a mapping sub-system. The tool includes an intermediate LIDAR sub-system and a distal LIDAR sub-system. The intermediate LIDAR sub-system has an intermediate emitter and an intermediate receiver. The intermediate emitter emits light and the intermediate receiver receives reflected light from the intermediate object. The distal LIDAR sub-system has a distal emitter and a distal receiver. The distal emitter emits light, and the distal receiver receives reflected light from the distal object. The mapping sub-system determines a position of the tool, determines an inner surface of the borehole, and generates and outputs a map of the borehole from the position and the inner surface. A method comprises steps performed during operation of the system.

System and method for mapping a borehole using lidar

Systems and methods for evaluating a subsurface region of the earth for hydrocarbon exploration, development, or production are disclosed. Embodiments of the present disclosure are configured to determine advanced radioactive formation data from commonly acquired well logging data sets. In particular, a predictive model is trained to generate 'synthetic' spectral gamma ray logs are from basic neutron, density and total gamma ray logs measured from a well within the formation. The predictive model comprises a neural network that is trained using multi-resolution graph clustering techniques to correlate patterns in the density, neutron and gamma ray log data to patterns in spectral gamma ray log data. Embodiments of the present disclosure are further configured to use the synthetic spectral gamma ray logs output by the model to quantify the clay content of the formation, its permeability and determine a hydrocarbon productivity index for the formation.

Growing community of inventors

Dhahran, Saudi Arabia

Ammar AlQatari