Machine-Learning-Assisted Identification of Steam Channeling after Cyclic Steam Stimulation in Heavy-Oil Reservoirs

Abstract

Cyclic steam stimulation (CSS) is one efficient technology for enhancing heavy-oil recovery. However, after multiple cycles, steam channeling severely limits the thermal recovery because high-temperature steam preferentially breaks through to the producers. To solve the issues of steam breakthrough, it is essentially important and necessary to recognize steam channeling. In this work, a machine-learning-assisted identification model, based on a random-forest ensemble algorithm, is developed to predict the occurrence of steam channeling during steam huff-and-puff processes. The set of feature attributes is constructed based on the permeability ratio, steam quality, and steam-injection speed, which provides the reference for the construction of the training-sample set, steam-channeling reconstruction set, and prediction set. Based on the realistic data, the Pearson correlation coefficient is implemented to confirm the linear correlation among different characteristics; thus, the dimension reduction of the characteristic parameters is achieved. The random oversampling method is adopted to treat the unbalanced training-sample set. Our results show that this model can accurately describe the current state of steam channeling and predict steam propagation in the following cycles.

Foam Systems for Enhancing Heavy Oil Recovery by Double Improving Mobility Ratio

“…These shortcomings make the exploitation of heavy oil seriously restricted. Therefore, varieties of methods [8,9], including in situ combustion [10][11][12], cyclic steam stimulation [13,14], steam flooding [15][16][17][18], steam-assisted gravity drainage (SAGD) [19][20][21], in situ aquathermolysis [22][23][24][25], etc., have been developed to reduce heavy oil viscosity to enhance the recovery. These methods employ increasing formation temperature to reduce the viscosity, which consumes much energy and has a high requirement on equipment.…”