Optimization of Exploration Prospects Based on Ant Colony Algorithm and XGBoost Combined Optimization Model

Traditional exploration prospect optimization is uncertain due to human factor, the primary reason of that problem is the complex nonlinear relationship between trap quality and related geological factors. Some researchers proposed use artificial neural network (ANN) to solve the problem of the comprehensive geological evaluation of traps, because ANN can describe the nonlinear relationship of multiple geological factors. Considering ANN has some drawbacks, such as it is need lots of parameters for training, and the learning process can not be observed. In this paper we proposed a combined optimization model to accomplish optimization of exploration prospects, and express the affinity order between the prospects and its related geological factors, also can provide the data support for exploration. Based on trap data of an oilfield in Africa, there are 12 geological factors related to trap quality, including trap coefficient, trap depth, trap scale, trap area, Reservoir coefficient, Preservation coefficient, hydrocarbon source coefficient, resources etc.. The ant colony algorithm is used for feature selection, and irrelevant and redundant features are eliminated through multiple iterations, making it suitable for model processing and improving training speed. Based on ant colony algorithm, we get the key parameters for XGBoost model training, namely trap area, reservoir coefficient, preservation coefficient, resource, and the key features are used in XGBoost model for training and prediction. Finally, we compared our prediction results with expert prediction, the error is 0. In this paper, we proposed a combined optimization model based on ant colony algorithm and XGBoost for exploration prospect optimization. We recognized the key geological factors and different characteristic rules for exploration prospect optimization, in the process of optimization, ant colony discards the bad features that interfere with classification and recognition, and retains the features that contribute greatly to classification. In comprehensive geological evaluate of trap, the proposed combined optimization model is suitable for complicated nonlinear geological relationship, and express the affinity order between the prospects, the proposed method can work as an auxiliary way in petroleum exploration, also the proposed method can provide decision support for exploration prospect optimization, and finally can fulfill cost decreasing and benefit increasing.

Transient Modeling of Plunger Lift for Gas Well Deliquification

Summary As an economical and efficient artificial lift method, plunger lift can be used to unload the accumulated liquids from the bottom of gas wells, which helps lower the bottomhole pressure, resulting in higher gas production rate. However, the transient flow behavior of the plunger-lift-aided production system is still not well understood due to the lack of a reliable and accurate prediction model. In this study, a transient mechanistic model is developed to simulate the comprehensive dynamic process of a plunger-lift system that is cyclically paced by a surface control valve. Starting from the Gasbarri and Wiggins (2001) dynamic plunger-lift model, four stages in the cyclic movement of a plunger can be identified and calculated using a set of specific governing equations. Considering the gas flows with a plunger in the tubing, the model can calculate the instant velocities of the plunger during its rising and falling movement. The classical inflow performance relationship (IPR) is employed as the reservoir model to obtain the fluid flow rates from the reservoir to the wellbore. The proposed new model can capture the essential parameters of plunger-lift cycles, including plunger velocity/acceleration, tubing/casing pressure, production rates, etc. Compared to previous models, the predicted rising and falling speeds of the plunger are improved. The hydrocarbon mixture properties in the gas well are computed by a compositional model in this study, which provides more accurate and reasonable predictions of tubing and casing pressure. Several parametric studies are presented in the paper. These studies will help to understand the influence of different parameters on the process of plunger lift. An appropriate combination of casing and tubing pressure should be taken into consideration. A reservoir coefficient term is introduced and defined. A larger reservoir coefficient will improve the ultimate profitability of the well by increasing the production rate at the beginning and accelerate the depletion of gas wells. If the gas/liquid ratio (GLR) is too low, liquid loading may be triggered. The parametric study shows that an adequate GLR is necessary for reliable plunger-liftperformance.