Intelligent agent injection method for online diagnosis of gas well liquid accumulation and foam drainage gas production

Abstract

The invention discloses an intelligent agent injection method for online diagnosis of gas well liquid accumulation and foam drainage gas production. The method comprises the following steps of collecting wellhead oil pressure, wellhead casing pressure, gas production rate and liquid production rate in real time, and calculating oil casing annulus pressure drop, oil pipe friction resistance pressure drop and oil pipe liquid carrying pressure drop based on the wellhead oil pressure, the wellhead casing pressure, the gas production rate and the liquid production rate; according to the oil casing annulus pressure drop, the oil pipe friction resistance pressure drop and the oil pipe liquid carrying pressure drop, obtaining the real-time oil pipe liquid accumulation amount by combining with the wellhead oil casing pressure difference; and adjusting the starting time of the current agent injection process and the corresponding target pump injection amount through the real-time oil pipe liquid accumulation amount. According to the invention, the accuracy of an online diagnosis result of the liquid accumulation amount and the stability of an intelligent injection agent system are improved, the intelligent decision-making level of foam drainage agent injection is improved, and agents are saved.

Description

technical field [0001] The invention relates to the technical field of petroleum and natural gas exploitation, in particular to a method for on-line diagnosis of liquid accumulation in a gas well and an intelligent injection method for foam liquid drainage and gas recovery. Background technique [0002] Foam liquid drainage gas recovery is the most important process to solve the problem of liquid accumulation in natural gas wells and maintain the stable production of gas wells in the middle and late stages. In recent years, under the development trend of digital gas fields and smart gas fields, the regular and quantitative filling mode relying on experience can no longer meet the production needs of gas fields, and the filling process has begun to advance in the direction of intelligence. [0003] The general idea of ​​bubble pad intelligent filling is "real-time diagnosis of fluid accumulation in gas wells, intelligent decision-making of dosing timing and target dosing volu...

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Problems solved by technology

[0004] However, the above existing methods have the following problems when they are applied to the field: (1) The pressure difference between the casing and the casing cannot directly reflect the amount of fluid accumulated in the downhole, but instead reflects the amount of fluid accumulated at the bottom of the well, the amount of liquid carried in the upper part of the wellbore, and the amount of fluid on the pipe wall. The overall size of the friction force

Directly using the pressure difference between oil and casing to convert the liquid column height will overestimate the amount of liquid accumulation, especially the continuous liquid-carrying well will be misjudged as a liquid accumulation well

(2) The wellbore of the fluid-loaded well shows intermittent drainage of slug flow, which leads to frequent fluctuations in the pressure difference data of the oil casing, which will cause repeated misjudgments of the fluid volume by the system. Based on the existing injection timing decision-making method, the real-time The downhole fluid volume is compared with the downhole fluid volume threshold and the main engine operation is started after exceeding the threshold. Therefore, this decision-making method will cause frequent start and stop of the pump and cannot effectively eliminate the downhole fluid volume.

(3) There is a period of reaction time from gas well dosing to effusion discharge. During this period, if the system adopts the existing decision-making method for dosing timing, it will continue to be judged as wellbore effusion, resulting in repeated excessive dosing. By prolonging the effusion judgment cycle Although repeated dosing can be avoided, the real-time performance of judgment becomes poor, the best timing for dosing will be missed, and the practical significance of intelligent dosing is lacking

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