公开(公告)号：US20250115804A1

公开(公告)日：2025-04-10

申请号：US18483781

申请日：2023-10-10

Applicant: ARAMCO SERVICES COMPANY

Inventor： Uchenna Odi ,  Nouf Jabri

IPC: C09K8/584 ,  E21B43/16

Abstract: A treatment fluid including an aqueous colloid containing a first surfactant and a plurality of nanoparticles encapsulated by a second surfactant. A method for preparing a treatment fluid including mixing a plurality of metallic oxide nanoparticles with a first surfactant to form an intermediate solution. A second surfactant is added to the intermediate solution to form nanoparticles encapsulated by the second surfactant. A method of extracting hydrocarbons from a well environment and storing carbon dioxide in the well environment including injecting a first amount of carbon dioxide and a first amount of a treatment fluid into a hydrocarbon reservoir via an injection well in a well environment. The treatment fluid treatment fluid including an aqueous colloid containing a first surfactant and a plurality of nanoparticles encapsulated by a second surfactant. Subsequently, determining a byproduct amount of the carbon dioxide extracted from the well environment.

公开(公告)号：US20250117557A1

公开(公告)日：2025-04-10

申请号：US18483833

申请日：2023-10-10

Applicant: ARAMCO SERVICES COMPANY

Inventor： Uchenna Odi ,  Nouf Jabri

IPC: G06F30/28 ,  G06F30/27

Abstract: A method of training an artificial intelligence (AI) model including obtaining M training pairs and training the AI model using the M training pairs. Obtaining the M training pairs includes inputting an mth set of injection values in a reservoir simulator and an mth set of output values from the reservoir simulator. A method of extracting hydrocarbons from and storing carbon dioxide in a formation including, for N cycles, inputting an nth set of output values into a trained AI model, obtaining an (n+1)th set of injection values from the trained AI model, injecting an (n+1)th amount of carbon dioxide and an (n+1)th amount of treatment fluid into a hydrocarbon reservoir based on the (n+1)th set of injection values, determining an (n+1)th set of output values from the formation, and rewarding or penalizing the trained AI model based on the nth set and the (n+1)th set of output values.