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In the shale oil & gas industry, operators are looking toward big data analytics to optimize operations and reduce cost. In this paper, we mainly focus on how to assist operators in understanding the subsurface formation, thereby helping them make optimal decisions. A large number of geology reports and well logs describing the sub-surface have been accumulated over years. Issuing geology reports is more time consuming and depends more on the expertise of engineers than acquiring the well logs. To assist in issuing geology reports, we propose an encoder-decoder-based model to automatically generate rock descriptions in human-readable format from multivariate well logs. Due to the different formats of data, this task differs dramatically from image and video captioning. The challenges are how to model structured rock descriptions and leverage the information in multivariate well logs. To achieve this, we design a hierarchical structure and two forms of attention for the decoder. Extensive validations are conducted on public well data of North Dakota in the United States. We show that our model is effective in generating rock descriptions. The two forms of attention enable the provision of a better insight into relations between well-log types and rock properties with our model from a data-driven perspective.

In the oil and gas industry, there is a growing demand for application of big data analytics and artificial intelligence (AI) technologies to optimize operations and reduce cost. In this study, we work on the productivity prediction, which is an important and challenging task for operators. Unlike previous studies where full field or single well analysis was conducted, we focus on more active operation units, drilling space units. Moreover, significant information is extracted from geology reports which are saved in scanned PDF files and well logs. By using the extracted information, long short-term memory (LSTM) model which could take the special-temporal changes of DSUs into consideration is employed to predict the DSUs' productivity. After rigorous validation, it is found that the accuracy of LSTM model could reach to more than 60%, which is 10% higher than a multilayer perceptron (MLP) model proposed in a previous research.

Kernel Principal Component Analysis (KPCA) is a technique widely used to understand and visualize non-linear variation patterns by inverse mapping the projected data from a high-dimensional feature space back to the original input space. Variation patterns often occur in a small number of relevant features out of the overall set of features that are recorded in the data. It is, therefore, crucial to discern this set of relevant features that define the pattern. Here we propose a feature selection procedure that augments KPCA to obtain importance estimates of the features given the noisy training data. Our feature selection strategy involves projecting the data points onto sparse random vectors for calculating the kernel matrix. We then match pairs of such projections, and determine the preimages of the data with and without a feature, thereby trying to identify the importance of that feature. Thus, preimages' differences within pairs are used to identify the relevant features. An advantage of our method is it can be used with any suitable KPCA algorithm. Moreover, the computations can be parallelized easily leading to significant speedup. We demonstrate our method on several simulated and real data sets, and compare the results to alternative approaches in the literature.

Manufacturing industries collect massive amounts of multivariate measurement through automated inspection processes. Noisy measurements and high-dimensional, irrelevant features make it difficult to identify useful patterns in the data. Principal component analysis provides linear summaries of datasets with fewer latent variables. Kernel Principal Component Analysis (KPCA), however, identifies nonlinear patterns. One challenge in KPCA is to inverse map the denoised signal from a high-dimensional feature space into its preimage in input space to visualize the nonlinear variation sources. However, such an inverse map is not always defined. This article provides a new meta-method applicable to any KPCA algorithm to approximate the preimage. It improves upon previous work where a strong assumption was the availability of noise-free training data. This is problematic for applications such as manufacturing variation analysis. To attenuate noise in kernel subspace estimation the final preimage is estimated as th...

In the shale oil & gas industry, operators are looking toward big data and new analytics tools and techniques to optimize operations and reduce cost. Formation evaluation is one of the most crucial steps before the fracturing operation. To assist engineers in understanding the subsurface and in turn make optimal operations, we focus on learning semantic relations between geology reports and well logs, which are collected during down-hole drilling. The challenges are how to represent the features of the geology reports and the well logs collected at measured depths and how to effectively embed them into a common feature space. We propose both linear and nonlinear (artificial neural network) models to achieve such an embedding. Extensive validations are conducted on public well data of North Dakota in the United States. We empirically discover that both geology reports and well logs follow a neighborhood property measured by geological distance. We show that this spatial information is highly effective in both the linear and nonlinear models and our nonlinear model with the spatial information performs the best among the state-of-the-art methods.