Tight oil fields
are affected by factors such as geology, technology,
and development, so it is difficult to directly obtain an accurate
recovery rate. The accurate prediction of the recovery rate is very
important for measuring reservoir development effects and dynamic
analysis. Traditional tight oil recovery predictions are obtained
by conventional formula calculations and curve fitting, which are
less applicable and very different from actual conditions. Machine
learning can make accurate predictions based on a large amount of
data, so it is used to predict the recovery rate of tight oil reservoirs.
The recovery rate of 200 wells in M tight oil reservoirs ranges widely
between 8.8 and 27.6%, with more than 14 factors affecting the recovery
rate, and the overall declining rule is not clear. Therefore, this
research combines the production data of horizontal wells with random
forest, support vector regression (SVR), and other methods, establishing
recovery prediction models to gain more accurate recovery predictions.
First, the Pearson correlation coefficient and the random forest (RF)
machine learning method are used to measure and calculate the degree
of nonlinear influence of factors on oil well recovery. Second, SVR
and optimization of support vector regression by particle swarm (PSO-SVR)
recovery prediction models are developed and tested, with 75% of the
data being used to train SVR and PSO-SVR recovery prediction models
and 25% to verify the model. Third, the accuracy of the results of
these two SVR oil recovery prediction models is compared, suggesting
that when the data are scarce, the optimized model is more accurate
than the unoptimized one by 10.85%. Thus, this model can assure a
relatively more accurate prediction of oil recovery. Machine learning
recovery prediction, being more accurate and applicable, enables the
data of factors such as construction and production systems to be
optimized in the future, enhancing the oil recovery rate.