5 results about "Mud weight" patented technology

Detection technology to monitor a flare stack during drilling operations and determine information based on the imaging. Various actions may be taken based on the determined information. For example, subterranean geology may be mapped. This may include mapping formation properties, fractures, faults or reservoir zones. Additionally, drilling operational parameters may be adjusted. This may include adjusting rate of direction, penetration, weight on bit, rotary speed, mud flow rate, mud weight, actuating a valve, or changing the pump speed.

A method for monitoring a risk to a stability of a wellbore in a subsurface formation includes receiving first input data 2024 / 243558 representing the wellbore or the subsurface formation. The method also includes extracting parameter-value pairs from the first input data. The method also includes determining an expected pore pressure gradient based upon the parameter-value pairs. The method also includes determining an expected fracture gradient based upon the parameter-value pairs. The method also includes determining a mud weight uncertainty profile for the wellbore based upon the expected pore pressure gradient and the expected fracture gradient.

The present application provides a kind of mud specific gravity automatic control system and control method, the control system includes stake machine unit, clean water pool and slurry pool, the stake machine unit is connected with slurry pool by first conveying device for conveying slurry, and the clean water pool is connected with stake machine unit by second conveying device for conveying clean water;The stake machine unit includes stake hole, stake machine is arranged above stake hole, mud specific gravity detection device is arranged in stake hole, the mud specific gravity detection device includes concentration meter, first gear, first sensor and first processor, first sensor is by collecting the angle of rotation of concentration meter drive first gear, and then the mud specific gravity in first processor is collected, and second conveying device is driven to carry out mud specific gravity adjustment.The present application ensures high-precision measurement of mud specific gravity, and further improves work efficiency through automatic control system.

The invention discloses nano excitation flow state solidified soil based on drilling muck, which is formed by mixing drilling muck slurry and a curing agent, the doping amount of the curing agent is 6%-12% of the weight of the drilling muck slurry, and the drilling muck slurry is formed by mixing drilling muck and water; wherein the curing agent comprises the following components in percentage by weight: 85%-92% of sulphoaluminate cement, 0.3%-1.0% of an underwater non-dispersing agent, 0.5%-2.0% of a nano early strength agent and 5%-10% of an active mineral admixture; the nanometer early strength agent is a nanometer C-S-H nucleating agent or nanometer lithium carbonate with the particle size smaller than or equal to 100 nm, through efficient excitation of the nanometer C-S-H nucleating agent or nanometer lithium carbonate, the 1d strength of the fluid-state solidified soil can reach 180 kPa, the 2d strength is larger than or equal to 150 kPa, the early strength is improved by 80% or above compared with traditional solidified soil, the transition time of a rotary drilling rig is shortened to 24-48 h, and the construction efficiency is greatly improved.

The present disclosure directs to a computer-implemented method for monitoring and treatment of a drilling fluid during a drilling operation by utilizing machine learning techniques. The method determines a mud density of the drilling fluid at a first predetermined time interval by measuring a mud weight (MW) of the drilling fluid. The method further performs a Marsh funnel (MF) measurement to assess a drilling fluid viscosity of the drilling fluid at the first predetermined time interval. The method further predicts a gel strength of the drilling fluid for a second predetermined time interval in real-time using an artificial neural network based on the measured mud density and the drilling fluid viscosity. The method further adjusts density of the drilling fluid to optimize well cuttings suspension and removal by adding an additive in response to the predicted gel strength.