Expert-guided machine learning has been used to classify depositional facies from core photographs of the Wolfcamp, Bone Spring and Spraberry formations in the Permian Basin. Training sets of core facies were selected by a sedimentologist. A model was built using a convolutional neural network and then tested against core outside of the training set with a 98% accuracy. The system can yield a quit-look of core facies much faster than that of traditional methods.
Artificial Intelligence (AI) is a branch of computer science that creates intelligent machines that work and react like humans. Machine learning is a key part of AI and requires an ability to identify patterns in streams of inputs. Learning with adequate supervision involves classification, which determines the category an object belongs to. Today it is being extensively used in image and speech recognition. At present the application of machine learning is in its infancy in the area of geosciences for the oil and gas industry.
The objective of our research is to determine if machine learning can be used to fast-track identification of depositional facies from images of conventional core photographs. Normally, this work requires a sedimentologist to painstakingly describe a core that may take many weeks to incorporate with logs and other formation evaluation data. With over 200 cored wells having 50,000 feet of core in our Permian Basin projects, the task of core description is overwhelming.
Theory and/or Methods
In order to meet the objective the software needs to be trained to recognize the various depositional facies. This is done by employing a sedimentologist (expert) to guide the training with the AI specialist. The sedimentologist builds a training set from several cores through a particular formation (e.g. Wolfcamp). The training set is a set of images selected by the sedimentologist to cover the range of depositional facies and the variations seen in each facies (Figure 1). The training sets typically employ 20 to 40 images of each facies.
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