Disclosed is an optimization method for dense cutting, temporary plugging and fracturing in shale horizontal well stage. The optimization method includes steps of obtaining reservoir parameters, completion parameters, and fracturing construction parameters, establishing a fluid-solid coupling model of hydraulic fracturing through a discontinuous displacement method, establishing a fracture propagation model for dense cutting, temporary plugging and fracturing in shale horizontal well stage, calculating geometric parameters of dense cutting, temporary plugging and fracturing fractures in shale horizontal well stage based on the reservoir parameters, the completion parameters, and the fracturing construction parameters, optimizing the construction parameters of dense cutting, temporary plugging and fracturing in shale horizontal well stage based on the geometric parameters of hydraulic fractures after dense cutting, temporary plugging and fracturing in stage and results temporary plugging operations.

A method for predicting oil flow rates is provided. The method includes accessing historical data from a plurality of databases, accessing historical perforation data and historical reservoir properties data from a simulation model, and determining fluid flow values and rock quality index values associated with perforated intervals of the plurality of wells. The method further includes corresponding the fluid flow values and rock quality values to the well production data, training, using the plurality of input values, a machine learning model for predicting oil flow values at perforated intervals of a plurality of target wells, predicting, using the trained machine learning model, the oil flow values at the perforated intervals of the plurality of target wells, and generating a synthetic production log that includes the predicted oil flow values at the perforated intervals of the plurality of target wells.

A system for deploying an untethered drone is provided. The system includes a wellbore drone for being deployed into a wellbore, a magazine unit, and a control system. The wellbore drone is configured to perform at least one action based on a control command which is provided from an on-board control system embedded in the wellbore drone. The magazine unit includes one or more chambers. The magazine unit is configured to retain the wellbore drone in a corresponding one of the one or more chambers, prior to deployment of the wellbore drone into the wellbore, and dispense the wellbore drone for being deployed into the wellbore through a launcher unit. The control system includes at least one control interface for controlling at least a part of operations of the wellbore drone and the magazine unit.

Methods and systems for evaluating the effectiveness of a cementing process including a wellbore having a liner disposed therein to create a flow path and an annulus within the wellbore. A cementing tool positionable at the surface of the wellbore for pumping a cement composition through the liner. A plurality of sensors dispersed throughout the cement composition and capable of transmitting a location signal receivable via a fiber optic cable. A control facility communicatively coupled with the fiber optic cable having one or more processors which execute instructions including receiving location data from the plurality of sensors via the fiber optic cable, and evaluating the location of each of the sensors to determine the effectiveness of the cementing process.

Exemplary embodiments are directed to a system and method for continuous downlinking communication from a surface location to a bottom hole assembly during drilling operations. The system transmits harmonic pressure wave fluctuations generated by a modulator, which is disposed outside of a surface-located fluid line with a flap rotatably disposed entirely inside of the fluid line encoding data by harmonics. One letter of the combinatorial frequencies signal alphabet can have more than 200 different orthogonal frequencies components; each component represents a unique combination of downlinking command purpose and value. For deepest portion of a long trajectory well, the system uses a narrow frequency range (2-3 Hz) with two letters resulting in more than 250 combinations. The system provides continuous automatic downhole control of the signal-to-noise ratio to achieve robust decoding of downlinking signals with a transmission data rate ten times faster as compared to 1-2 bits per minute in the industry.

Methods and systems for monitoring conditions within a wellbore of a subterranean well include extending a drill string into the subterranean well from a terranean surface. The drill string has an actuator assembly, a sensor compartment, and a plurality of sensors located within the sensor compartment. The actuator assembly is instructed to transmit a swarm release signal to a central power unit of the sensor compartment so that the central power unit of the sensor compartment releases certain of the plurality of sensors from the sensor compartment. Data from the sensors is transferred to a data processing system after the sensors reach the terranean surface.

Embodiments of the invention provide an untethered apparatus for measuring properties along a subterranean well. According to at least one embodiment, the untethered apparatus includes a housing, and one or more sensors configured to measure data along the subterranean well. The data includes one or more physical, chemical, geological or structural properties in the subterranean well. The untethered apparatus further includes a processor configured to control the one or more sensors measuring the data and to store the measured data, and a transmitter configured to transmit the measured data to a receiver arranged external to the subterranean well. Further, the untethered appratus includes a controller configured to control the buoyancy or the drag of the untethered apparatus to control a position of the untethered apparatus in the subterranean well. The processor includes instructions defining measurement parameters for the one or more sensors of the untethered apparatus within the subterranean well.

This invention discloses a multi-phase flow simulation analysis method based on generalized mobility, which comprises the following steps: S1: The generalized mobility describes fluid flow laws in different subset of study area by using the generalized mobility models with the same form; S2: On the basis of generalized mobility, the multi-component multi-phase flow simulation equations are established. Through solving the above mentioned multi-component multi-phase flow simulation equations, the pressure, temperature, saturation, and mole percentage of each component and each phase of multicomponent multiphase flow fluids in study area are obtained; S3: The corresponding application software are formed by using the established multi-component multi-phase flow simulation and analysis equations. The invention plays an important role in solving the single and multi-well flow simulation of complex multicomponent multiphase flow reservoirs, multi-well interference analysis, deliverability analysis, transient pressure analysis, transient rate analysis, transient temperature analysis, well test design, and permanent downhole monitoring data analysis.

A system is described for estimating well production and injection rates of a subterranean reservoir using machine learning models. The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. The processor may receive a set of static geological data about at least one subterranean reservoir in a subterranean formation. The processor may apply a trained convolutional neural network to the set of static geological data and data on initial states of dynamic reservoir properties to determine dynamic outputs of the subterranean reservoir. The processor may determine well data by extracting the set of static geological data and the dynamic outputs at well trajectories. And, the processor may apply a trained artificial neural network to the well data and subterranean grid information about the subterranean reservoir to generate estimated well production and injection rates.

An optimization apparatus for optimizing an objective function of a resource production system, comprising: an analytical model of the resource production system configured to receive data from the resource production system, the analytical model comprising a reservoir model, a plurality of well models coupled to the reservoir model, and a surface pipeline model coupled to the plurality of well models, wherein the plurality of well models comprise a first set of segments associated with a first set of operating parameters; and an optimization module configured to vary a set of variable parameters of the analytical model and satisfy a set of pre-determined constraints to optimize an objective function of the resource production system, wherein running the optimization includes calculating pressures of each of the first set of segments based on the analytical model. An optimization method and system are also described.