Data mining and machine learning for identifying sweet spots in shale reservoirs

Due to its complex structure, production form a shale-gas formation requires more drillings than those for the traditional reservoirs. Modeling of such reservoirs and making predictions for their production also require highly extensive datasets. Both are very costly. In-situ measurements, such as well-logging, are one of most indispensable tools for providing considerable amount of information and data for such unconventional reservoirs. Production from shale reservoirs involves the so-called fracking, i.e. injection of water and chemicals into the formation in order to open up flow paths for the hydrocarbons. The measurements and any other types of data are utilized for making critical decisions regarding development of a potential shale reservoir, as it requires hundreds of millions of dollar initial investment. The questions that must be addressed include, does the region under study can be used economically for producing hydrocarbons? If the response to the first question is affirmative, then, where are the best places to carry out hydro-fracking? Through the answers to such questions one identifies the sweet spots of shale reservoirs, which are the regions that contain high total organic carbon (TOC) and brittle rocks that can be fractured. In this paper, two methods from data mining and machine learning techniques are used to aid identifying such regions. The first method is based on a stepwise algorithm that determines the best combination of the variables (well-log data) to predict the target parameters. However, in order to incorporate more training, and efficiently use the available datasets, a hybrid machine-learning algorithm is also presented that models more accurately the complex spatial correlations between the input and target parameters. Then, statistical comparisons between the estimated variables and the available data are made, which indicate very good agreement between the two. The proposed model can be used effectively to estimate the probability of targeting the sweet spots. In the light of an automatic input and parameter selection, the algorithm does not require any further adjustment and can continuously evaluate the target parameters, as more data become available. Furthermore, the method is able to optimally identify the necessary logs that must be run, which significantly reduces data acquisition operations.