Temizel, Cenk (Aera Energy) | Balaji, Karthik (University of North Dakota) | Canbaz, Celal Hakan (Ege University) | Palabiyik, Yildiray (Istanbul Technical University) | Moreno, Raul (Smart Recovery) | Rabiei, Minou (University of North Dakota) | Zhou, Zifu (University of North Dakota) | Ranjith, Rahul (Far Technologies)
Due to complex characteristics of shale reservoirs, data-driven techniques offer fast and practical solutions in optimization and better management of shale assets. Developments in data-driven techniques enable robust analysis of not only the primary depletion mechanisms, but also the enhanced oil recovery in unconventionals such as natural gas injection. This study provides a comprehensive background on application of data-driven methods in oil and gas industry, the process, methodology and learnings along with examples of data-driven analysis of natural gas injection in shale oil reservoirs through the use of publicly-available data.
Data is obtained and organized. Patterns in production data are analyzed using data-driven methods to understand key parameters in the recovery process as well as the optimum operational strategies to improve recovery. The complete process is illustrated step-by-step for clarity and to serve as a practical guide for readers. This study also provides information on what other alternative physics-based evaluation methods will be able to offer in the current conditions of data availability and the understanding of physics of recovery in shale oil assets together with the comparison of outcomes of those methods with respect to the data-driven methods. Thereby, a thorough comparison of physics-based and data-driven methods, their advantages, drawbacks and challenges are provided.
It has been observed that data organization and filtering takes significant time before application of the actual data-driven method, yet data-driven methods serve as a practical solution in fields that are mature enough to bear data for analysis as long as the methodology is carefully applied. The advantages, challenges and associated risks of using data-driven methods are also included. The results of comparison between physics-based methods and data-driven methods illustrate the advantages and disadvantages of each method while providing the differences in evaluation and outcome along with a guideline for when to use what kind of strategy and evaluation in an asset.
A comprehensive understanding of the interactions between key components of the formation and the way various elements of an EOR process impact these interactions, is of paramount importance. Among the few existing studies on natural gas injection in shale oil with the use of data-driven methods in oil and gas industry include a comparative approach including the physics-based methods but lack the interrelationship between physics-based and data-driven methods as a complementary and a competitor within the era of rise of unconventionals. This study closes the gap and serves as an up-to-date reference for industry professionals.
ABSTRACT: It is distinctly important to precisely detect and quantify the micro-cracks in shale rock sample, which serve as the potential fluid flow conduits. In the SEM scanning images, because of the low contrast of the micro-cracks and background and extremely central grayscale value distribution, the segmentation is always insufficient. In this paper, a transformed operator, incomplete beta function, is applied and the simulated annealing method is used to optimize the parameters --α and β. Simulated annealing method, which is a self-adaptive algorithm, can provide different optimal values depending on different input images. The result of the classical segmentation technique – Otsu’s method, acts as the evaluation parameter. After the processing, the target becomes more obvious by human vision. In addition, the grayscale value distribution is expanded, and is not restricted to the high value range only. Most importantly, the contrast of target and background increases from 0.1216 to 0.2118 and the volume fraction of micro-cracks increases from 1.25 % to 4.02 %, which show that the segmentation after processing is more accurate. All the observation and evaluation parameters show the strong ability of image enhancement studied in this paper.
Bakken Oilfield, characterised by low permeability (0.04 md) and low porosity (5%), has attracted great attention because of its huge reserve (Jia, B. et al., 2017; Wang, H. et al., 2018). As a result, it is of vital importance to detect the micro-cracks in the shale rock, which are potential fluid flow conduits (Kong, L. et al., 2018; Ling, K. et al., 2016). In recent years, with the development of high-resolution SEM scanning techniques, the quantification of micro-cracks has become feasible. However, for some scanning images, the target is not obvious enough to be segmented from the background. Image enhancement is one of the most effective measures to solve this problem.
Image enhancement for the small target has been one of the hottest topics of image processing. The goal of image enhancement is to improve the image quality, which can improve the human vision ability and provide a better image processing result (Wang, D.C., 1983). The traditional image enhancement techniques include spatial smoothing, histogram equalization (Hum, Y. C. et al., 2014), histogram matching (Gonzalez, R.C. et al., 2008), edge enhancement and filtering (Reuter, M. et al., 2009) et al. For most of these techniques mentioned above, either the processing algorithm is linear, which result in the insufficient contrast of target and background grayscale after processing, or requires too much human intervention. Some automatic image processing techniques have been developed in recent years, including the application of gamma function (Wang, D.C. et al., 1983) and finite Rayleigh distribution (Cocklin, M. L. et al., 1983).
In this paper, an automatic grayscale transformed algorithm based on the cumulative distribution of the grayscale value of input image – Incomplete Beta Function (Tubbs, J.D., 1987), is applied. The incomplete Beta function transformation belongs to the parametric image enhancement, and the parameters will be optimised using simulated annealing method (Kirkpatrick, G., 1983).
Balaji, Karthik (University of North Dakota) | Rabiei, Minou (University of North Dakota) | Canbaz, Hakan (Schlumberger) | Agharzeyva, Zinyat (Texas A & M University) | Tek, Suleyman (University of the Incarnate Word) | Bulut, Ummugul (Texas A&M University-San Antonio) | Temizel, Cenk (Aera Energy LLC)
Data-driven methods serve as a robust tool to turn data into knowledge. Historical data generally has not been used in an effective way in analyzing processes due to lack of a well-organized data, where there is a huge potential of turning terabytes of data into knowledge. With the advances and implementation of data-driven methods data-driven models have become more widely-used in analysis, predictive modeling, control and optimization of several processes. Yet, the industry overall is still skeptical on the use of datadriven methods, since they are data-based solution rather than traditional physics-based solutions; even though physics and geology are sometimes part of this methodology. This study comprehensively evaluates the status of data-driven methods in oil and gas industry along with the recent advances and applications.
This study outlines the development of these methods from the fundamentals, theory and applications of these methods along with their historical acceptance and use in the industry. Major challenges in the process of implementation of these methods are reviewed for different areas of applications. The major advantages and drawbacks of data-driven methods over the traditional methods are discussed. Limitations and areas of opportunities are outlined. Latest advances along with latest applications and the associated results and value of the methods are provided.
It is observed that the successful use of data-driven methods requires strong understanding of petroleum engineering processes and the physics-based conventional methods together with a good grasp of traditional statistics, data mining, artificial intelligence and machine learning. Data-driven methods start with a data-based approach to identify the issues and their solutions. Even though data-driven methods provide great solutions on some challenging and complex processes, that are new and/or hard to define with existing conventional methods, there is still skepticism in the industry on the use of these methods. This is strongly tied to the delicacy and sensitive nature of the processes and on the usage of the data. Organization and refinement of the data turn out to be important components of an efficient data-driven process.
Data-driven methods offer great advantages in the industry over that of conventional methods under certain conditions. However, the image of these methods for most of the industry professionals is still fuzzy. This study serves to bridge the gap between successful implementation and more widely use and acceptance of data-driven methods, and the fuzziness and reservations on the understanding of these methods in the industry. Significant components of these methods along with clarification of definitions, theory, application and concerns are also outlined in this study.
To block big pore throats and channels, caused by high-temperature steam, in vicinity of injection wells of steam flooding, profile control agent should be inject. For the optimum effect, its properties should include excellent heat resistance and some permeability after consolidation, which is beneficial for improvement of sweep coefficient. In this paper, we do a study on how to synthesize a novel granular profile control agent and evaluate its properties. The basic procedure includes synthesis material selection, synthesis method optimization, consolidation properties test (setting time, consolidation strength and heat resistance) by orthogonal experiments and simulation experiments by heterogeneous parallel dual-core flooding tests. Results show that profile control agent is self-cemented, inorganic particle with the scale of mm, matching the scale of big pore throats and channels in vicinity of injection well.
Excess water production is a serious economic and environmental problem in most mature oil fields. Accurate and timely diagnosis of the water production mechanism is critical in the success of the applied treatment methodology. While many empirical techniques have been traditionally used in production data analysis, the significance of water-oil ratio (WOR) in proper identification of the type of the water production problem in oil wells is not yet fully investigated. Data mining techniques could facilitate extracting any hidden predictive information from oil and water production data to be used in water control studies.
This paper applies a meta learning classification technique called Logistic Model Trees (LMT) to diagnose water production mechanisms based on WOR data and static reservoir parameters. Synthetic reservoir models are built to simulate excess water production due to coning, channeling and gravity segregated flows. Various cases are then generated by varying some of the input parameters in each model. A number of key features from plots of WOR against oil recovery factor are heuristically extracted by segmenting these plots at certain points. LMT classifiers are then applied to integrate these features with reservoir parameters to build classification models for predicting the water production mechanism in different scenarios of pre and post water-production stages.
It is observed that a valid association between WOR data and the water production mechanism exists. Our results with high prediction accuracy rates of 88% for pre-production and more than 94% for post-production stage demonstrate efficiency of the proposed LMT classifiers and significance of WOR values in classifying excess water production problems.