Reservoirs and the lateral seal of stratigraphic traps are controlled by the depositional environment or diagenesis. The recognition of facies and lithology from seismic attributes is an effective approach for identifying stratigraphic traps related to the depositional environment. In this paper, the occurrence of stratigraphic traps related to depositional environment in Permian aeolian clastics and Jurassic carbonate-evaporites was studied. To identify these stratigraphic traps, multiple seismic attributes were classified using supervised and unsupervised artificial neural networks (ANNs), which allowed the recognition of seismic facies and lithology.
Neural networks are a powerful classification technique, which incorporates multiple attributes into a number of classes to identify sedimentary facies. Two algorithms comprising supervised and unsupervised neural networks are commonly implemented. With a supervised learning algorithm, prior information such as typical facies at the control wells are required to train the multilayer perceptron (MLP) network. With an unsupervised algorithm, only seismic data is input to the neural network, and competitive-learning techniques are employed to classify or self-organize the data based on its internal characteristics. Without prior information, the output classes are not labeled with lithofacies. According to the availability of prior information, supervised and unsupervised learning were applied to recognize dune-playa and carbonate-evaporite combinations, respectively. To characterize the depositional environments, joint interpretation with a geological model is necessary for both supervised and unsupervised classification.
Two major findings have been derived from this work. First, the learning technology based on ANNs is effective to recognize sedimentary facies. The microfacies and lithologies identified by both supervised and unsupervised ANNs are very consistent with the drilled wells. Second, the recognition of depositional facies and lithology can characterize the stratigraphic traps in the study areas. Lateral seal plays a key role in stratigraphic traps. Playa siltstone and tight lagoonal limestone constitute the lateral seal in dune-playa and carbonate-evaporite combinations, respectively.
Si-Hai Zhang*, Mahdi Abu-Ali, and Yin Xu, EXPEC Advanced Research Center, Saudi Aramco Summary The stratigraphic traps in Unayzah formation play an important role in central Saudi Arabia where aeolian sandstone with good reservoir quality is laterally sealed by up-dip playa siltstone with low-porosity and lowpermeability due to the facies change. Seismic waveforms are employed using a supervised artificial neural network (ANN) to classify and identify the seismic facies. Important lateral variations in reflector continuity is blended into the waveform classification map in order to improve the facies recognition. The predicted distribution of facies and lithology is consistent with well data and characterizes the stratigraphic traps in central Saudi Arabia. Introduction The objective of this work is to try to characterize the stratigraphic traps in central Saudi Arabia through seismic facies characterization.