In this study, using a multi-layer perceptron neural network (MLPNN) model, total organic carbon (TOC) and hydrogen index (HI) values for Pabdeh and Gurpi Formations in the oil fields of Naft Sefid (NS-13), Kupal (KL-36, KL-38, and KL-48) and Palangan (PL-2) were calculated in the North Dezful Embayment located in the southwest of Iran. To build the MLPNN model, the geochemical data calculated by the Rock–Eval pyrolysis method (TOC and HI) and the conventional petrophysical well log data, including sonic transit time log (DT), formation density log (RHOB), total resistivity log (RT), spectral gamma-ray log, computed gamma-ray log and neutron porosity log from the NS-13 well were used. The log data were the input layer, and the geochemical data were the output layer of the model. Twenty-four datasets were used for MLPNN training, and seven datasets were used for MLPNN testing. Two hidden layers were considered in this technique. Each hidden layer has an activation function (tanh) and a solver parameter (lbfgs). The accuracy of measurement of TOC and HI indices of Pabdeh and Gurpi Formations in terms of R² was 0.93 and 0.90, respectively. This model has higher accuracy than the ΔlogR technique (R²: 0.28). Considering the relationships between the input data and other wireline logs is an advantage of this technique. These two formations have five source rock zones. Pabdeh Formation has three zones. The middle zone of the Pabdeh Formation (Pz. II) has the highest TOC (2.6 wt%) and source rock potential. Pabdeh Formation has kerogen type II. Gurpi Formation has a weaker source rock potential than Pabdeh Formation due to its low TOC content (< 1%). Both source rock zones of this formation have low TOC, but in some layers of the lower zone of the Gurpi Formation (Gz. II), high values for TOC were predicted. Gurpi Formation has Kerogen types II and III.

在本研究中，使用多层感知器神经网络 (MLPNN) 模型，计算了 Naft Sefid (NS-) 油田 Pabdeh 和 Gurpi 地层的总有机碳 (TOC) 和氢指数 (HI) 值13)、Kupal (KL-36、KL-38 和 KL-48) 和 Palangan (PL-2) 是在位于伊朗西南部的 North Dezful Embayment 进行计算的。为了建立MLPNN模型，Rock-Eval热解法计算的地球化学数据（TOC和HI）和常规石油物理测井数据，包括声波时差测井（DT）、地层密度测井（RHOB）、总电阻率测井（使用了 NS-13 井的能谱伽马射线测井、计算伽马射线测井和中子孔隙度测井。测井数据为模型的输入层，地球化学数据为模型的输出层。 24 个数据集用于 MLPNN 训练，7 个数据集用于 MLPNN 测试。该技术考虑了两个隐藏层。每个隐藏层都有一个激活函数（tanh）和一个求解器参数（lbfgs）。 Pabdeh组和Gurpi组TOC和HI指数测量精度R²分别为0.93和0.90 ， 分别。该模型比 ΔlogR 技术具有更高的精度（R²：0.28）。考虑输入数据和其他有线日志之间的关系是该技术的优点。这两个地层有五个烃源岩带。 Pabdeh 组有三个区域。 Pabdeh 组 (Pz. II) 的中部区域具有最高的 TOC (2.6 wt%) 和烃源岩潜力。 Pabdeh 组具有 II 型干酪根。由于 TOC 含量低（< 1%），Gurpi 组的烃源岩潜力弱于 Pabdeh 组。该地层的两个烃源岩区域的 TOC 均较低，但在 Gurpi 组 (Gz. II) 下部区域的某些层中，预计 TOC 值较高。 Gurpi 组干酪根类型为 II 型和 III 型。