A computational stratigraphy model may be run for M mini-steps to simulate changes in a subsurface representation across M mini-steps (from 0-th subsurface representation to M-th subsurface representation), with a mini-step corresponding to a mini-time duration. The subsurface representation after individual steps may be characterized by a set of computational stratigraphy model variables. Some or all of the computational stratigraphy model variables from running of the computational stratigraphy model may be provided as input to a machine learning model. The machine learning model may predict changes to the subsurface representation over a step corresponding to a time duration longer than the mini-time duration and output a predicted subsurface representation. The subsurface representation may be updated based on the predicted subsurface representation outputted by the machine learning model. Running of the computational stratigraphy model and usage of the machine learning model may be iterated until the end of the simulation.

The present invention discloses a model test system and method for seabed seismic wave detection. The model test system includes a model test unit which specifically includes a testbed for simulating a seabed, wherein a module for simulating bedrock and geology is arranged in the testbed; a water source supply unit supplies simulation seawater into the testbed; a sea wave generation apparatus is configured to act on the simulation seawater at different speeds and different forces to generate different sizes of sea waves. The present invention provides support and guidance for the advancing investigation of geologic parameters in a detection area such as distribution situations of faults, a range of landslides, a depth and morphology of a glide plane and the like.

A method of predicting behavior of a drilling assembly includes: generating a mathematical representation of a geometry of each of a plurality of components of a drilling assembly, the plurality of components including a plurality of cutters and one or more additional components configured to at least one of: support the plurality of cutters and operably connect the plurality of cutters to the drill string, the one or more additional components including a drill bit crown; simulating one or more operating conditions incident on the drilling assembly representation, and simulating an interaction between the plurality of components and an earth formation; and predicting physical responses of the drilling assembly representation to the one or more conditions.

A method of drilling a well includes receiving depth specific drill parameters associated with drilling wells within a field; receiving drilling report entries associated with drilling the wells within the field; applying a report analysis engine to the drilling report entries to determine a drilling event; determining a field-specific drilling model utilizing the depth specific drill parameters and drilling event; prescribing a drilling recipe using a drilling analysis engine applied to the field-specific drilling model; and drilling the well in accordance with the drilling recipe.

The present disclosure relates to a method and system for evaluating the macro resilience of offshore oil well control equipment. The method for evaluating the macro resilience of offshore oil well control equipment comprises six steps: determining the type and strength of external disaster; calculating the failure rate of components; calculating the recovery rate of the components; modeling the BN for a degradation process; modeling the BN for a maintenance process; and calculating the resilience of the offshore oil well control equipment. A system for evaluating the macro resilience of offshore oil well control equipment comprises an external disaster evaluation module, a component failure rate calculation subsystem, a reliability degradation process simulation module, a fault identification module, a component recovery rate calculation module, a reliability recovery process simulation module, a reliability change curve derivation unit and an resilience calculation unit.

The present disclosure relates to the field of oil recovery technologies in an oilfield, and discloses a method for calculating recovery ratio under a secondary-tertiary combination development mode. The method includes: obtaining a sweep efficiency of water used in the process of a primary oil recovery E.sub.V1 and an oil displacement efficiency of the primary oil recovery E.sub.D1; performing a secondary development of water flooding using injection-production conditions of the primary oil recovery, and obtaining an increment percentage of the E.sub.V1, , caused by the secondary development of water flooding; directly performing a tertiary oil recovery using injection-production conditions of the primary oil recovery, and obtaining an increment percentage of the E.sub.V1, , caused by the tertiary oil recovery and an oil displacement efficiency of the tertiary oil recovery E.sub.D2; establishing a calculation model of a sweep efficiency of displacement media used in the process of the secondary-tertiary combination development E.sub.V2+3 according to E.sub.V1, and ; and calculating an increment in a recovery ratio under the secondary-tertiary combination development mode relative to a recovery ratio of the primary oil recovery E.sub.R2+3. The increment in the recovery ratio E.sub.R2+3 as determined in the present disclosure takes the advantage of the secondary development of water flooding that has an integrated well network and the advantage of the tertiary oil recovery that can increase the oil displacement efficiency, and thereby can be used for theoretically understanding the increased value of the recovery ratio under the secondary-tertiary combination development mode.

Provided herein are systems and methods for modeling and simulating reservoir, wellbore, and hydraulic fracturing. The systems and methods provided herein may facilitate well life cycle simulation by integrating a three-dimensional model representative of hydraulic fracturing and fluid flow in a wellbore and reservoir. The systems and methods may couple fluid flow in the wellbore and reservoir during injection and extraction with propagation of fractures through subsurface materials during fluid injection. Integrated three-dimensional reservoir, wellbore, and hydraulic fracture simulation may be useful for the design of hydraulic fracture treatments and prediction of future reservoir production.

Methods and systems for modeling and predicting fractures within the subsurface region are provided. The methods and systems use multi-layer models to represent stacks of layered rocks, which are used to evaluate shear tractions caused by the relatively higher lateral strains in a more compliant overlying or underlying adjacent layers. As the lateral strains can induce tensional stresses in the brittle layers that can exceed the rocks tensile strength and fail, the formation of natural fractures may be modeled. Accordingly, the method and system model fractures due to stacking using mechanical rock property information from well logs and simulating the farfield loading conditions using basin history.

Methods for using shut-in pressures to determine uncertainties in a hydraulic fracturing process in a shale reservoir are described. Data commonly collected during multistage fracturing is used to calculate propped fracture height and induced stresses, as well as other variables, in the presence of horizontal stress anisotropy. These variables can then be incorporated into reservoir simulations to improve the fracturing monitoring, forecast hydrocarbon recoveries, or modify fracturing plans.

Systems and methods include a method providing automated production optimization for smart well completions using real-time nodal analysis including real-time modeling. Real-time well rates and flowing bottom-hole pressure data are collected by a multilateral well optimizing system at various choke settings for multiple flow conditions for each lateral of a multilateral well during regular field optimization procedures. Surface and downhole pressures and production metrics for each of the laterals are recorded for one lateral at a time. A multilateral well production model is calibrated using the surface and downhole pressures and the production metrics for each of the laterals. Flowing parameters of individual laterals are estimated using the multilateral well production model. An optimum pressure drop across each downhole valve is determined using the multilateral well production model. A productivity of each lateral is estimated using the model during the commingled production at various choke valves settings.