Basin modeling and thermal maturity estimation are crucial for understanding sedimentary basin evolution and hydrocarbon potential. Assessing thermal maturity in the oil and gas industry is vital during exploration. With artificial intelligence advancements, more accurate evaluation of hydrocarbon source rocks and efficient thermal maturity estimation are possible. This study employed 1D basin modeling using PetroMod and a novel hybrid group method of data handling (GMDH) neural network optimized by a differential evolution (DE) algorithm to estimate thermal maturity (Tmax) and assess kerogen type in Triassic–Jurassic source rocks of the Mandawa Basin, Tanzania. The GMDH–DE addresses the limitations of conventional methods by offering a data-driven approach that reduces computational time, overcomes overfitting, and improves accuracy. The 1D thermal maturity basin modeling suggests that the Mbuo source rocks reached the gas–oil window in late Triassic times and began expulsion in the early Jurassic while located in an immature-to-mature zone. The GMDH–DE model effectively estimated Tmax with high coefficient of determination (R² = 0.9946), low root mean square error (RMSE = 0.004), and mean absolute error (MAE = 0.006) during training. When tested on unseen data, the GMDH–DE model yielded an R² of 0.9703, RMSE of 0.017, and MAE of 0.025. Moreover, GMDH–DE reduced the computational time by 94% during training and 87% during testing. The results demonstrated the model’s exceptional reliability compared to the benchmark methods such as artificial neural network–particle swarm optimization and principal component analysis coupled with artificial neural network. The GMDH–DE Tmax model offers a unique and independent approach for rapid real-time determination of Tmax values in organic matter, promoting efficient resource assessment in oil and gas exploration.

盆地建模和热成熟度估算对于了解沉积盆地演化和油气潜力至关重要。评估石油和天然气行业的热成熟度在勘探过程中至关重要。随着人工智能的进步，更准确的烃源岩评估和高效的热成熟度估算成为可能。本研究采用 PetroMod 的一维盆地建模和通过差分进化 (DE) 算法优化的新型混合组数据处理方法 (GMDH) 神经网络来估计热成熟度 (Tmax) 并评估三叠纪-侏罗纪烃源岩中的干酪根类型。坦桑尼亚曼达瓦盆地。 GMDH-DE 通过提供数据驱动的方法来解决传统方法的局限性，该方法可以减少计算时间、克服过度拟合并提高准确性。一维热成熟度盆地模型表明，Mbuo 烃源岩在三叠世晚期达到油气窗口，并在早侏罗世开始排出，同时位于未成熟到成熟区域。 GMDH-DE 模型在训练过程中有效地估计了 Tmax，具有高决定系数 (R ² = 0.9946)、低均方根误差 (RMSE = 0.004) 和平均绝对误差 (MAE = 0.006)。当对未见过的数据进行测试时，GMDH–DE 模型得出的 R ² 为 0.9703，RMSE 为 0.017，MAE 为 0.025。此外，GMDH-DE 在训练期间将计算时间减少了 94%，在测试期间减少了 87%。结果表明，与人工神经网络粒子群优化和主成分分析结合人工神经网络等基准方法相比，该模型具有卓越的可靠性。 GMDH-DE Tmax 模型提供了一种独特且独立的方法，可以快速实时测定有机质中的 Tmax 值，从而促进石油和天然气勘探中的高效资源评估。