通过混合MABC-LSSVM模型通过井记录数据进行孔径预测
Wei Su1, Jie Gao1, Wensheng Wu1
1State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China.
PloS one
|October 27, 2025
概括
预测页岩水库的多孔性是一项挑战. 使用修改的人工蜜蜂群和最小方格支向量机的新型混合模型 (MABC-LSSVM) 显著提高了孔径预测的准确性.
科学领域:
- 石油地质科学 石油地质科学
- 人工智能在水库工程中的应用
- 数据驱动的地质建模
背景情况:
- 准确的孔隙性预测对于水库性能评估至关重要,尤其是在复杂的页岩层中.
- 页岩水库在记录数据和孔隙性之间表现出强烈的异质性和非线性关系,挑战了传统的预测方法.
- 现有的方法往往缺乏概括能力和精度.
研究的目的:
- 开发一种混合智能模型,用于在复杂的页岩水库中高精度的孔径预测.
- 通过优化机器学习超参数来利用数据驱动的方法.
- 提高石油和天然气勘探中的水库评估准确度.
主要方法:
- 开发了一种混合模型,将修改的人工蜜蜂群 (MABC) 优化算法与最小平方支向量机 (LSSVM) 结合起来.
- 在MABC算法优化LSSVM超参数使用惯性重量和加速度系数.
- 输入参数包括补偿中子日志 (CNL),密度日志 (DEN),光电吸收截面指数 (PE) 和玛射线日志 (GR).
主要成果:
- 与LSSVM,GBDT和ABC-LSSVM相比,MABC-LSSVM模型表现出优异的预测性能.
- 预测结果显示与真度曲线具有很高的一致性.
- 确定系数 (R2) 达到0.93,明显超过了对比模型的表现.
结论:
- 智能优化算法与LSSVM的集成对于复杂的形成孔隙性预测是有效的.
- 在具有挑战性的页岩环境中,MABC-LSSVM方法提供了一种可靠的水库评估方法.
- 这种数据驱动的方法提高了在石油和天然气行业中孔隙性预测的准确性和适用性.
相关概念视频
Porosity and Absorption of Aggregate
732
Aggregates contain pores of varying sizes; while some are completely enclosed within the particles, others open onto the surface, allowing water to penetrate. The porosity of aggregates is a major factor contributing to the overall porosity of concrete, given that aggregates constitute about three-quarters of concrete's volume.
When all pores in an aggregate are filled with water, the aggregate is considered saturated and surface-dry. If left in dry air, water will evaporate until the...
When all pores in an aggregate are filled with water, the aggregate is considered saturated and surface-dry. If left in dry air, water will evaporate until the...
732
Multicompartment Models: Overview
497
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
497
Porosity in Cement Paste
428
The porosity of concrete is a measure of the void spaces within its structure. These spaces impact its strength and durability significantly. When water and cement interact, a chemical reaction called hydration creates a semi-solid paste. This paste includes combined water, making up approximately 23% of the cement's dry mass, and gel water, which fills minuscule voids known as gel pores, accounting for about 28% of the cement gel volume.
The balance of water to cement in the mix is...
The balance of water to cement in the mix is...
428
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
1.1K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
1.1K
Model Approaches for Pharmacokinetic Data: Compartment Models
519
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Two primary types of compartment models are recognized: mammillary and catenary. The more...
519
Mechanistic Models: Compartment Models in Individual and Population Analysis
241
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
241

