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

Highlights

• An automatic procedure that can use most of the available information.

• The proposed method can be updated rapidly when new data are available.

• Data can be integrated with different scales and lengths.

• Sweet-spot location can be identified using all available data.

Abstract

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.

Introduction

Due to the recent progress in multistage hydraulic fracturing, horizontal drilling and advanced recovery methods, shales, recognized as unconventional reservoirs, have become a promising source of energy. They are formed by fine-grained organic-rich matters and were previously considered as source and seal rock that, due to gas production and high pressures in the conventional reservoirs were traditionally called the trouble zones. Such regions were usually ignored, which explains why no comprehensive data are available for them. Thus, their characterization is still a massive task (Tahmasebi et al., 2015a, Tahmasebi et al., 2015b, Tahmasebi et al., 2016b, Tahmasebi and Sahimi, 2015, Tahmasebi et al., 2015a). Furthermore, shales exhibit highly variable structures and complexities from basin to basin, and even in small fields. They host very small pores, and have low-matrix permeability and heterogeneity, both at the laboratory and field scales. Due to such difficulties and given the fact that new methods for characterization of shale reservoirs are still being developed, application of the characterization and modeling methods for the traditional reservoir to shales is of great importance. In particular, accurate characterization of such reservoirs entails integrating various information, including petrophysical, geochemical, geomechanical, and reservoir data (Tahmasebi and Sahimi, 2016a, Tahmasebi and Sahimi, 2016b Tahmasebi et al., 2016a, Tahmasebi et al., 2017, Tahmasebi et al., 2016c).

Apart from its type, efficient drilling may be thought of as targeting the most productive zones of a reservoir with maximum exposure. For shale reservoirs, this concept is equivalent to areas with high total organic carbon (TOC) and high fracability, i.e. brittleness, which calls for comprehensive characterization of such complex formations. High TOC and fracable index (FI) reflect high quality of shale-gas reservoirs. The role of the TOC is clear, as it is one of the main factors for identifying an economical shale reservoir. Higher TOC, ranging from 2% to 10%, represents richer organic contents and, consequently, higher potential for gas production. Since natural gas is trapped in both organic and inorganic matters, the TOC denotes the entire organic carbons, and is a direct measure of the volume and maturity of the reservoir.

The FI influences the flow of hydrocarbons in a shale reservoir and any future fracking in it. Thus, identifying the layers in a reservoir with high FI is of great importance. The FI controls a shale reservoir's production since it strongly influences the wells’ production. It also provides very useful insight into where and how new wells should be placed and spaced. In fact, unlike conventional reservoirs that depend on long-range connectivity of the permeable zones, optimal well locations and spacing control the performance of shale reservoirs and future fracking operations in them. Thus, separating the brittle and ductile zones of rock is a key aspect of successful characterization of shale-gas reservoirs. Moreover, brittle shale has high potentials for being naturally fractured and, consequently, exhibits good response to fracking treatments.

Past successful experience indicated that characterization of shale reservoirs need accurate identification of the so-called sweet spots, i.e. the zones that present the best production or the potential for high production, and the potential fracable zones, which are critical to maximizing the production and future recovery. The placement of most of the wells is closely linked with the sweet spots, as well as the fracable zones for hydraulic fracturing. For example, the TOC represents the ability of a shale reservoir in storing and producing hydrocarbons. Fracability is controlled mainly by mineralogy and elastic properties, such as the Young's and bulk moduli and the Poisson's ratio (Sullivan Glaser et al., 2013). Therefore, identification of the sweet spots is of great importance to shale reservoirs. Such spots are characterized through high kerogen content, low water saturation, high permeability, high Young's modulus and low Poisson's ratio.

One of the primary, as well as most affordable, methods for characterizing complex reservoirs is coring and collecting petrophysical data, as well as well logs. The latter can be integrated with former in order to develop a more reliable model. In principle, well-log data can be provided continuously and, thus, they represent a real-time resource. Because of a huge number of wells in a typical shale reservoir, we refer to such datasets as big data. Clearly, such information is very useful when it is coupled with some techniques that help better identify the sweet spots and fracable zones. Eventually, the questions that must be addressed are: where one should/should not drill new wells? Where are the zones with high/low fracability index?

Aside from such critical questions, another issue regarding the available big data is the fact that new data are continuously obtained as the production proceeds. Thus, any algorithm for the analysis of big data should be flexible enough for rapid adaptation of new data. Furthermore, another important feature of the algorithm should be its ability to use the available information to create a “training platform” for forecasting the important parameters.

In this paper, a very large database consisting of well logs, x-ray diffraction (XRD) data, and experimental core analysis is used to develop a model that reduces the cost and increases the probability of identifying the sweet spots. First, we describe a method of data mining called stepwise regression (Efromyson, 1960, Montgomery et al., 2012) for identifying the correlations between the target (i.e., dependent) parameters – the TOC and FI - and the available well-log data (the independent variables). Then, a hybrid method borrowed from machine learning and artificial intelligence is proposed for accurate predictions of the parameters. Both methods can be tuned rapidly, and can use the older database to accurately characterize shale reservoirs.