In an attempt to reduce the errors and uncertainties associated with predicting reservoir properties for static modeling, seismic inversion was integrated with artificial neural network for improved porosity and water saturation prediction in the undrilled prospective area of the study field, where hydrocarbon presence had been confirmed. Two supervised neural network techniques (MLFN and PNN) were adopted in the feasibility study performed to predict reservoir properties, using P-impedance volumes generated from model-based inversion process as the major secondary constraint parameter. Results of the feasibility study for predicted porosity with PNN gave a better result than MLFN, when correlated with well porosity, with a correlation coefficient of 0.96 and 0.69, respectively. Validation of the prediction revealed a cross-validation correlation of 0.88 and 0.26, respectively, for both techniques, when a random transfer function derived from a given well is applied on other well locations. Prediction of water saturation using PNN also gave a better result than MLFN with correlation coefficient of 0.97 and 0.57 and cross-validation correlation coefficient of 0.89 and 0.3, respectively. Hence, PNN technique was adopted to predict both reservoir properties in the field. The porosity and water saturation predicted from seismic in the prospective area were 24–30% and 20–30%, respectively. This indicates the presence of good quality hydrocarbon bearing sand within the prospective region of the studied reservoir. As such, the results from the integrated techniques can be relied upon to predict and populate static models with very good representative subsurface reservoir properties for reserves estimation before and after drilling wells in the prospective zone of reservoirs.

为了减少与静态模型预测储层性质相关的误差和不确定性，将地震反演与人工神经网络相集成，以改善已证实油气存在的研究区域未钻探前瞻区域的孔隙度和水饱和度预测。在可行性研究中采用了两种监督神经网络技术（MLFN和PNN），以基于模型的反演过程产生的P阻抗体积作为主要的次要约束参数，来预测储层性质。当与井眼孔隙率相关时，用PNN预测孔隙度的可行性研究结果给出了比MLFN更好的结果，相关系数分别为0.96和0.69。当从给定井获得的随机传递函数应用于其他井位置时，两种方法的预测验证结果均显示交叉验证相关性分别为0.88和0.26。使用PNN预测水饱和度也比MLFN更好，相关系数分别为0.97和0.57，交叉验证相关系数分别为0.89和0.3。因此，采用了PNN技术来预测油田中的两种储层特性。根据地震预测，该地区的孔隙度和含水饱和度分别为24％至30％和20％至30％。这表明在研究储层的预期范围内存在优质含烃砂。因此，