Vida Gholami

Grid-Based Surrogate Reservoir Modeling (SRM) for Fast Track Analysis of Numerical Reservoir Simulation Models at the Gridblock Level

Developing proxy models has a long history in our industry. Proxy models provide fast approximated solutions that substitute large numerical simulation models. They serve specific useful purposes such as assisted history matching and production/injection optimization. Most common proxy models are either reduced models or response surfaces. While the former accomplishes the run-time speed by grossly approximating the problem the latter accomplishes it by grossly approximating the solution space. Nevertheless, they are routinely developed and used in order to generate fast solutions to changes in the input space. Regardless of the type of model simplifications that is used, these conventional proxy models can only provide, at best, responses at the well locations, i.e. pressure or rate profiles at the well. In this paper we present application of a new approach to building proxy models. This method has one major difference with the traditional proxy models. It has the capability of replicating the results of the numerical simulation models, away from the wellbores. The method is called Grid-Based Surrogate Reservoir Model (SRM) since it is has the unique capability of being able to replicate the pressure and saturation distribution throughout the reservoir at the grid block level, and at each time step, with reasonable accuracy. Grid-Based SRM performs this task at high speed, when compared with conventional numerical simulators such as those currently in use (commercial and in-house) in our industry. To demonstrate the capabilities of Grid-Based SRM, its application to three reservoir simulation models are presented. Fist is a giant oil field in the Middle East with a large number of producers, second, to a CO2 sequestration project in Australia, and finally to a numerical simulation study of potential carbon storage site in the United States. The numerical reservoir simulation models are developed using two of the most commonly used commercial simulators 1 . Two of the models presented in this manuscript are consisted of hundreds of thousands of grid blocks and one includes close to a million cells. The Grid-based SRM that learns and replicates the fluid flow through these reservoirs can open new doors in reservoir modeling by providing the means for extended study of reservoir behavior with minimal computational cost. Surrogate Reservoir Modeling (SRM) is

Artificial Intelligence (AI) Assisted History Matching

History matching is the process of adjusting uncertain reservoir parameters until an acceptable match with the measured production data is obtained. Complexity and insufficient knowledge of reservoir characteristics makes this process timeconsuming with high computational cost. In the recent years, many efforts mainly referred as assisted history matching have attempted to make this process faster; nevertheless, the degree of success of these techniques continues to be a subject for debate. This study aims to examine the application of a unique pattern recognition technology to improve the time and efforts required for completing a successful history matching project. The pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM) are used to develop Surrogate Reservoir Model (SRM) for utilization as the engine to drive the history matching process. SRM is an intelligent prototype of the full-field reservoir simulation model that runs in fractions of a second. SRM is built using a handful of geological realizations. In this study, a synthetic reservoir model of a heterogeneous oilfield with 24 production wells and 30 years of production history was used as the ground truth (the subject and the goal of the history match). An SRM was created to accurately represent this reservoir model. The history matching process for this field was performed using the SRM and by tuning static data (Permeability). The result of this study demonstrates the capabilities of SRM for fast track and accurate reproduction of the numerical model results. Speed and accuracy make SRM a fast and effective tool for assisted history matching.

Using Big Data and Smart Field Technology for Detecting Leakage in a CO2 Storage Project

Smart Fields are distinguished with two characteristics: Big Data and Real-Time access. A small smart field with only ten wells can generate more than a billion data points every year. This data is streamed in real-time while being stored in data historians. The challenge for operating a smart field is to be able to process this massive amount of information in ways that can be useful in reservoir management and relevant operations. In this paper we introduce a technology for processing and utilization of data generated in a smart field. The project is CO2 storage in Citronelle Dome, Alabama and the objective is to use smart field technology to build a real-time, long-term CO2 Intelligent Leakage Detection System (ILDS). The main concern for geologic CO2 sequestration is the capability of the underground carbon dioxide storage to confine and sustain the injected CO2 for very long time. If a leakage from a geological sink occurs, it is crucial to find the approximate location and amount of the leak in order to take on proper remediation activity. To help accommodate CO2 leak detection, two PDGs (Permanent Down-hole Gauges) have been installed in the observation well. A reservoir simulation model for CO2 sequestration in the Citronelle Dome was developed. Multiple scenarios of CO2 leakage is modeled and high frequency pressure data from the PDGs in the observation well are collected. The complexity of the pressure signal behaviors and the reservoir model makes the use of inverse solution of analytical models impractical. Therefore an alternate solution is developed for the ILDS, based on Machine Learning. High Frequency Data Streams are processed in real-time, summarized (by Descriptive Statistics) and transformed into a format appropriate for pattern recognition technology. Successful detection of location and amount of CO2 leaking from the reservoir using the real-time data streams demonstrates the power of pattern recognition and machine learning as a reservoir and operational management tool for smart fields.

A Field Study on Simulation of CO2 Injection and ECBM Production and Prediction of CO2 Storage Capacity in Unmineable Coal Seam

CO2 sequestration into a coal seam project was studied and a numerical model was developed in this paper to simulate the primary and secondary coal bed methane production (CBM/ECBM) and carbon dioxide (CO2) injection. The key geological and reservoir parameters, which are germane to driving enhanced coal bed methane (ECBM) and CO2 sequestration processes, including cleat permeability, cleat porosity, CH4 adsorption time, CO2 adsorption time, CH4 Langmuir isotherm, CO2 Langmuir isotherm, and Palmer and Mansoori parameters, have been analyzed within a reasonable range. The model simulation results showed good matches for both CBM/ECBM production and CO2 injection compared with the field data. The history-matched model was used to estimate the total CO2 sequestration capacity in the field. The model forecast showed that the total CO2 injection capacity in the coal seam could be 22,817 tons, which is in agreement with the initial estimations based on the Langmuir isotherm experiment. Total CO2 injected in the first three years was 2,600 tons, which according to the model has increased methane recovery (due to ECBM) by 6,700 scf/d.

Application of Artificial Intelligence and Data Mining Techniques for Fast Track Modeling of Geologic Sequestration of Carbon Dioxide - Case Study of SACROC Unit

The complexities involved in the available reservoir simulation model for the geologic CO2 sequestration study at SACROC Unit, lead to a high computational cost nearly impractical for different types of reservoir studies. In this study, as an alternative to the full-field reservoir simulation model, we develop and examine the application of a new technology (Surrogate Reservoir Model – SRM) for fast track modeling of pressure and phase saturation distributions in the injection and post-injection time periods. The SRM is developed based on a few realizations of full-field reservoir simulation model, and it is able to generate the outputs in a very short time with reasonable accuracy. The SRM is developed using the pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM) techniques. The SRM is trained based on the provided examples of the system and then verified using additional samples. The intricacy of simulating multiphase flow, having large number of time steps required to study injection and post-injection periods of CO2 sequestration, highly heterogeneous reservoir, and a large number of wells have led to a highly complicated reservoir simulation model for SACROC Unit. A single realization of this model takes hours to run. An in-depth understanding of CO2 sequestration process requires multiple realizations of the reservoir model. Consequently, using a conventional numerical simulator makes the computational cost of the analysis too high to be practical. On the other hand, the developed SRM for this case study runs in a matter of seconds. The comparison between the results of SRM and simulator, during training and verification steps of SRM development, demonstrates the ability of SRM in mimicking the behavior of numerical simulation model. The results of this study are intended to prove the potential of AI&DM based reservoir models, like SRM, to ease the obstacles involved in the conventional CO2 sequestration modeling.

Intelligent Upscaling of Static and Dynamic Reservoir Properties

Rock typing is an essential part of building geological model for an asset. Millions of dollars are invested in logs, core measurements, SCAL studies and geological interpretation that result in definition of different rock types. In most caes rock types that are identified in a reservoir do not have crisp boundaries and display overlapping characteristics. During the upscaling process, multiple rock types that have been identified in a high resolution geological (geo-cellular) model are approximated into a dominant rock type for any grid block in a reservoir simulation flow model.This defeats the original purpose of performing detail geological and petrophysical studies as far as reservoir flow models are concerned. The objective of this study is to develop a new upscaling methodology based on fuzzy set theory principles . Fuzzy rock typing refers to taking into account the inherent uncertainties and vagueness associated with rock typing in hydrocarbon bearing reservoirs. In this paper a numerical simulator has been used as the control environment in order to set up multiple studies that would demonstrate the difference between using conventional approach of implementation of geological models in the reservoir flow simulation studies and the new approach that is the subject of this study. By using the numerical reservoir simulator as the control environment it is intended to study the complexities that exist in geological model at much higher resolution and then compare simulation in large grid blocks using two different approaches. The problem has been set up in the form of upscaling study. First, the study was performed using conventional upscaling practices and then it was carried out using the proposed technique. The results then have been analyzed in order to demonstrate the difference between the two techniques and the advantages and disadvantages o f each have been identified. This manuscript has been organized in several sections. In the literature review section the current practices in upscaling dynamic reservoir properties such as relative permeability and capillary pressure are reviewd as well as basic definitions of rock typing. A brief section on fuzzy set theory is also presented in this section. The problem is defined in more details and some background information is presented in the “Introduction” Section. The approaches used to perform the study are presented in the “Methodology” section. Details results are presented and discussed in the “Results & Discussion” section and the manuscript is completed providing some concluding remarks.

Production Analysis of a Niobrara Field Using Intelligent Top-Down Modeling

Unconventional hydrocarbon resources are going to play an important role in the US energy strategy. Conventional tools and techniques that are used for analysis of unconventional resources include decline curve analysis, type curve matching and sometimes (in the case of prolific assets) reservoir simulation. These methods have not been completely successful due to the fact that fluid flow in unconventional reservoirs does not follow the same physical principles that supports mentioned analytical and numerical methods. Application of an innovative technology, Top-Down Modeling (TDM), is proposed for the analyses of unconventional resources. This technology is completely data-driven, incorporating field measurements (drilling data, well logs, cores, well tests, production history, etc.) to build comprehensive full field reservoir models. In this study, a Top-Down Model (TDM) was developed for a field in Weld County, Colorado, producing from Niobrara. The TDM was built using data from more than 145 wells. Well logs, production history, well design parameters and dynamic production constrains are the main data attributes that were used to perform data driven analysis. The workflow for TopDown Modeling included generating a high-level geological model followed by reservoir delineation based on regional productivity, reserve and recovery estimation, field wide pattern recognition (based on fuzzy set theory), Key Performance Indicator (KPI) analysis (which estimates the degree of influence of each parameter on the field production), and finally history matching the production data from individual wells and production forecasting. The results of production analysis by Top-Down Modeling can provide insightful guidelines for better planning and decision making.

A Parametric Study and Economic Evaluation of Drilling Patterns in Deep, Thick CBM Reservoirs

Over the past decade, production from unconventional reservoirs such as coalbed methane has increased dramatically. The focal driving force for this growth in coalbed methane production was the development and promulgation of reservoir engineering and completion technology. There have been many studies performed on the well configuration and production optimization techniques in the coal seams. According to many of these studies horizontal, deviated and multi lateral wells are of more benefit compared to the vertical wells. However, the target of these studies has mostly been the thin coal seams hence this result might not hold for the thick CBM reservoirs. The experience has proved that one thick coal is better than an equivalent thickness of multiple thin coals. Samples of the deep thickest accumulation of coal in the world can be found in some of the areas of the United States such as Uintah basin and Piceance basin in Colorado, Black Warrior basin in Alabama and Arkoma basin in Oklahoma and Alaska. The thickness of coal beds in these areas can reach up to 150 ft and they can be as deep as 9000 ft. Although horizontal drilling will result in higher gas production and consequently more revenue, the drilling cost in these wells are significantly higher than the vertical wells. When the coal beds are thick enough that the contact of wellbore and formation is not a limiting issue, the practicality of drilling horizontally versus multiple vertical wells might be questioned. In this work, the most appropriate drilling patterns in thick and deep CBM reservoirs have been identified. A sensitivity analysis has been performed with the intention of addressing the effect of different reservoir parameters and well configurations germane to methane production from CBM. These characteristics include fracture permeability, fracture porosity, gas content, and desorption time.The yardstick for comparing the economical practicality of different drilling configurations under diverse reservoir properties is the Net Present Value (NPV).