Core flow experiments are a key means of studying fluid flow patterns in underground rocks. From raw data to drawing effective conclusions, rigorous data processing, calibration, and interpretation processes are required.

Data processing: Removing falsehood and preserving truth

Raw data often contains noise and outliers. For example, pressure sensors may experience instantaneous fluctuations due to electromagnetic interference, and flow meter readings may deviate due to fluid pulsation. For this, filtering algorithms such as moving average filtering, median filtering, etc. need to be used to smooth the data curve and eliminate high-frequency noise. At the same time, statistical methods are used to identify and eliminate outliers, such as based on the 3 σ principle, data that exceeds the mean by three times the standard deviation is considered abnormal and eliminated to ensure data quality.

Data correction: precise restoration

There are differences between the experimental conditions and the actual underground environment, which need to be corrected. In terms of temperature correction, the viscosity of the fluid changes significantly with temperature, which affects the flow rate. It is necessary to calibrate the flow data using the viscosity temperature relationship equation (such as Arrhenius equation) based on the experimental temperature and formation temperature. The pressure calibration method considers the pressure loss of the experimental device, such as pipeline friction, valve throttling, etc. By establishing a pressure loss model, the pressure gradient measured in the experiment is restored to the true pressure difference at both ends of the rock core.

Data interpretation: insight into patterns

Based on the corrected data and combined with rock physical parameters such as porosity and permeability, explain the seepage law. By drawing a pressure flow curve, analyze whether the seepage conforms to Darcy's law and determine the type of fluid flow (linear seepage or nonlinear seepage). Furthermore, using numerical simulation software, experimental data and rock parameters are input to construct a permeability model, predict the recovery rate under different development plans, and provide scientific basis for the formulation of oilfield development strategies.

Through systematic data processing, calibration, and interpretation, core flow experimental data can be transformed into valuable information to reveal underground seepage mechanisms and guide oil and gas development.