Soodabeh Esmaili

Fast Track Analysis of Shale Numerical Models

Latest advances in shale gas reservoir simulation and modeling have made it possible to optimize and enhance the production from organic rich shale gas reservoirs. Reservoir simulator is no longer used with a simple description of the complex shale gas reservoirs, but with multiple, equally probable realizations to allow risk assessment. Nevertheless, the perennial challenge in shale reservoir modeling is to strike a balance between explicit representation of reservoir complexity and long simulation run time for multiple realizations. Focus of this study is on the development, calibration and validation of a Shale Surrogate Reservoir Model (AI-based proxy model) that represents a series of complex shale numerical simulation models. The Shale Surrogate Reservoir Model is then used for fast track analysis of the shale numerical model. Reservoir simulation model for a generic shale gas reservoir are constructed using a popular commercial simulator that is capable of handling complex fracture network (natural and multiple stages of hydraulic fractures), different sorption types (instantaneous and time dependent one), and capturing long transient nature of the flow in shale matrix. Validation of the Shale Surrogate Reservoir model using several blind simulation runs is also presented. Shale Surrogate Reservoir Model is a replica for the numerical simulation model with response that is measured in fractions of a second. As such, it provides the means for comprehensive and fast track analysis of the model in a relatively short period of time, allowing the reservoir engineer to scrutinize different realizations and propose development strategies. Introduction Numerical models capable of modeling the most important features of tight gas and gas shales are undergoing further development to include better representations of the basic physics controlling gas flow as the industry learns more. (Lee and Sidle, 2010) Various attempts have been made to model flow in shale gas systems. Models describing detailed physical processes can be built in a numerical setting where fractures can be discretely modeled and matrix blocks are assigned to transfer gas through diffusion and desorption into the fracture blocks (e.g. Lewis, 2004; Cipolla et al. 2009, Rubin 2010; Wang 2011; Mongalvy 2011). Many difficulties on shale gas modeling caused engineers to rely heavily on the simplest, most accessible tool: such as using a reduced physics models (Wilson et al.2012) or using the simplest production data analysis approach( e.g. Decline curve analysis) by knowing the fact that tools of traditional production data analysis have not been sufficient identifying flow behavior in shale system. At the same time, other researchers try to present some conditions of the design parameters that should be considered to build proper and more accurate shale gas simulator. Civan et al. (2011) proposed a quad-porosity approach accounting for a complicated reservoir pore structure that includes pores in the organic matter, inorganic matter; natural and hydraulic fractures with heterogeneous wettability, and different relative permeability and capillary pressure functions.

Geomechanical Properties of Unconventional Shale Reservoirs

Production from unconventional reservoirs has gained an increased attention among operators in North America during past years and is believed to secure the energy demand for next decades. Economic production from unconventional reservoirs is mainly attributed to realizing the complexities and key fundamentals of reservoir formation properties. Geomechanical well logs (including well logs such as total minimum horizontal stress, Poisson’s ratio, and Young, shear, and bulk modulus) are secured source to obtain these substantial shale rock properties. However, running these geomechanical well logs for the entire asset is not a common practice that is associated with the cost of obtaining these well logs. In this study, synthetic geomechanical well logs for a Marcellus shale asset located in southern Pennsylvania are generated using data-driven modeling. Full-field geomechanical distributions (map and volumes) of this asset for five geomechanical properties are also created using general geostatistical methods coupled with data-driven modeling. The results showed that synthetic geomechanical well logs and real field logs fall into each other when the input dataset has not seen the real field well logs. Geomechanical distributions of the Marcellus shale improved significantly when full-field data is incorporated in the geostatistical calculations.

Forecasting, Sensitivity and Economic Analysis of Hydrocarbon Production from Shale Plays Using Artificial Intelligence & Data Mining

Our understanding of the complexities of the flow mechanism in Shale plays has not kept up with our industry’s interest in these prolific and hydrocarbon rich formations. Furthermore, massive multi-cluster, multi-stage hydraulic fractures, that have proven to be essential for economic recovery from Shale plays, have significantly increased the complexity of the flow behavior and consequently have made the modeling efforts more challenging. In this paper, the application of a recently developed AI (Artificial Intelligence)-based reservoir modeling approach on Marcellus Shale is presented. In this approach, data mining and pattern recognition techniques were used to initiate modeling of the hydrocarbon production (dray gas and condensate) from Marcellus Shale. Instead of imposing our understanding of flow and transport in shale gas media, which is a very complex and non-linear system, we allow the production history, reservoir characteristics, and hydraulic fracturing data and operational constraint to force their will on our model and determine its behavior. In this work, the full-field history matching process was performed on a Marcellus shale asset including 135 wells with multiple pads and different landing targets. The full field AI-based Marcellus Shale model then used for forecasting the future well/reservoir performance to assist in planning field development strategies. The goodness of match quality is selfevident, thereby validating this modeling approach. Nevertheless, to examine the model validity in the forecasting mode, the field data was partially matched and then attempted forecasting. Taking validation one-step further, the production performance of a recently drilled well, which was completely blind to the model (was not involved during training and initial validation), was predicted and compared with actual field measurement. Furthermore, sensitivity and economic analysis are performed in order to identify the impact of different reservoir descriptions (e.g. different reservoir characteristics, stimulation and completion factors) and rank the impact of abovementioned parameters on the Net Present Value (NPV) of investing on gas wells producing from Marcellus Shale. Introduction Shale gas has attracted attention throughout the world. As a result, there has been a lot of research on the shale gas reservoirs focusing toward improving the understanding of the flow mechanism especially in pore scale, adsorbed gas, lithofacies and mineralogy identification and finally upsacling the physics to macro scale that can be used in numerical simulation model. On the other hand, hydraulic fracture initiation and propagation, which is essential in productivity of shale plays, is subject of many researches. Different studies have been done trying to incorporate the stimulation zone in flows simulation (e.g. by wing longitudinal fracture and Stimulated reservoir volume).Still there is a debate on what’ happinening to the more than 60 to 70% trapped injected water. Are they really act as a proppant (C.A Economides, 2011) or they cause formation damage? As stated by Swami and Settari, 2012, the equations and mathematical models developed for conventional sandstone and carbonate hydrocarbon reservoirs (pore size range 1100 micron) are not applicable for shale with pores at nanoscale. For

Modeling and History Matching of Hydrocarbon Production from Marcellus Shale Using Data Mining and Pattern Recognition Technologies

The Marcellus Shale play has attracted much attention in recent years. Our full understanding of the complexities of the flow mechanism in matrix, sorption process and flow behavior in complex fracture system (natural and hydraulic) still has a long way to go in this prolific and hydrocarbon rich formation. In this paper, we present and discuss a novel approach to modeling and history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining & pattern recognition technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, and hydraulic fracturing data to force their will on our model and determine its behavior. The uniqueness of this technique is that it incorporates the so-called “hard data” directly into the reservoir model, such that the model can be used to optimize the hydraulic fracture process. The “hard data” refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. The study focuses on part of Marcellus shale including 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full-field history matching process was completed successfully. Artificial Intelligence (AI)based model proved its capability in capturing the production behavior with acceptable accuracy for individual wells and for the entire field. Introduction Shale gas reservoirs pose a tremendous potential resource for future development, and study of these systems is proceeding apace. Shale gas reservoirs in particular possess many so-called “unconventional” features and considerations, on macroand micro-scales of flow (Freeman e al.2011). Shale reservoirs are characterized by extremely low permeability rocks that have a number of unique attributes, including high organic content, high clay content, extremely fine grain size, plate-like micro-porosity, little to no macro-porosity, and coupled Darcy and Fickian flow through the rock matrix. In contrast with conventional and even tight sandstone gas reservoirs where all the gas in the pore space is free gas, the gas in shale is stored by compression (as free gas) and by adsorption on the surfaces of the solid material (either organic matter or minerals) as well (Guo et al.2012). This combination of traits has led to the evolution of hydraulic fracture stimulation involving high rates, low-viscosities, and large volumes of proppant. The stimulation design for plays such as Marcellus Shale is drastically different than anything else that has been performed in the past. It takes large amounts of space, materials, and equipment to treat the Marcellus

Using Data-Driven Analytics to Assess the Impact of Design Parameters on Production from Shale

The importance of production from Shale and its impact on the total US energy equation has focused much attention on this prolific source of hydrocarbon. Consequently, research related to unconventional reservoirs has increased significantly in order to better understand the inherent complexities of their behavior. Analytical, numerical and statistical analyses have been applied to large multi-variable data set from Shale assets with different degrees of success. The notion that shale is a “statistical play” may be attributed to the fact that many of our preconceived notions on storage and flow mechanisms in shale are not supported by facts. Therefore, we set out to examine the possibility of learning from the data in order to be able to answer some of the questions that rise during the production process. Data Driven Analytics, having roots in pattern recognition and machine learning, have proven to be capable of extracting useful information from large data sets and are extensively used in many industries. Their application to multivariable data sets from Shale assets, in order to extract understandable structure in the data, is the subject of the work being presented here. This paper presents a Data Driven Analytics study of design parameters such as well trajectories, completion, and hydraulic fracturing variables for a large number of horizontal wells in Marcellus Shale. The data set from the Shale assets is so complex that use of conventional statistical analysis does not results in understandable trends and patterns. On the other hand, when advanced pattern recognition tools are used, certain (previously hidden) patterns emerges from the data with unmistakable trends. In this article impact of parameters such as up-dip versus down dip deviation of wells, stimulated lateral length and cluster spacing, etc. on production from wells in a Shale asset is analyzed using an advance pattern recognition algorithm. The analyses are performed using production from multiple time intervals throughout the life of wells.

Which Parameters Control Production in Shale Assets? A Pattern Recognition Study

Production from Shale assets has become one of the most significant sources of US domestic energy today. Consequently, research related to unconventional reservoirs has increased significantly in order to better understand the inherent complexities of their behavior. Analytical, numerical and statistical analyses have been applied to large multi-variable data set from Shale assets with different degrees of success. Pattern Recognition Technologies, having roots in machine learning, have proven to be capable of extracting useful information from large data sets and are extensively used in many industries. Their application to multi-variable data sets from Shale assets, in order to extract understandable structure in the data, is the subject of this article. This paper presents a pattern recognition study of well locations and trajectories, reservoir characteristics, completion, hydraulic fracturing, and production parameters for a large number of horizontal wells in Marcellus Shale. The data set from the shale assets is so complex that use of conventional statistical analysis does not results in understandable trends and patterns. On the other hand, when advanced pattern recognition tools are used, certain (previously hidden) patterns emerges from the data with unmistakable trends. In this article impact of parameters such as up-dip versus down dip deviation of wells, TOC, porosity, stimulated lateral length and cluster spacing, etc. on production from wells in a shale asset is analyzed using an advance pattern recognition algorithm. The analyses are performed using production from multiple time intervals throughout the life of wells. Introduction The production of shale gas in the US brought into being a major energy source at the beginning of this century and has increased significantly since. According to the International Energy Agency (IEA), in 2010, shale gas represented more than 20% of the country’s gas production, and it is estimated that by 2035 around 40% of the world’s gas might be unconventional, and shale gas will be the greatest part of it. The development of shale gas production was prompted by technological advances particular concerning horizontal drilling and hydraulic fracturing. While these practices have led to the economical productions of natural gases in numerous shale gas reservoirs, the problem of understanding shale gas production has been much involved due to the complicated and unpredicted response of these reservoirs to fluid and proppant injection. Besides, each of the shale gas properties such as thickness of the productive layer and geomechanical properties of rock vary substantially within the same producing area and this variability of shale gas properties greatly influences the well performance. Understanding the performance of such ultra-low permeable media creates new challenges to scientists. Investigating the impact of rock properties and evaluating the effect of hydraulic fracturing process on well performance are the primary issues that have been addressed in several studies.