Against the backdrop of continuously advancing big data technology

digitization and intelligent production management of oil and gas fields have become an inevitable trend. Shale gas wells face challenges such as liquid accumulation

sand production

and high stress sensitivity

necessitating the consideration of numerous factors during production adjustment. Traditional methods

which require extensive manual labor and exhibit low efficiency in production adjustment warnings

fail to consider multiple factors for optimal adjustment. To address this issue

considering the typical lifecycle characteristics of shale gas wells

the study divided the lifecycle into three stages: liquid unloading and gas transportation

stable production and pressure reduction

and fixed pressure and production reduction. Specific production adjustment rules tailored to different lifecycle stages were defined and six types of production adjustment indicators were proposed

including pressure drop method in stable production period

gas production per unit pressure drop method

critical sand-carrying flow rate method

critical liquid-carrying flow rate method

empirical chart method

and intermittent well-switching method. Based on these production adjustment rules and indicators

combined with field experience

an intelligent production adjustment early warning model for shale gas wells using fuzzy logic control was established. Implemented in Python

this model allows for real-time calculation of all production adjustment indicators and the fuzzy logic control algorithm as the well production dynamics change. This method has been applied in over a hundred wells in the Weirong shale gas field

reducing early warning times for production adjustments to just 30 seconds

compared to the traditional methods that require at least 5 days. The timely early warning of production adjustment needs has achieved good application results. In the future

combining remote control of oil nozzles or choke valves to adjust the production regime can provide technical support for intelligent oil and gas fields.