May 8, 2014


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Please join the reservoir Study Group for this one day forum to review the latest topics in Reservoir Engineering. The Reservoir Technology Forum is a one-day event designed to disseminate the knowledge and technology needed to achieve the many objectives of reservoir management, including understanding risk, increasing production and reserves, and maximizing recovery. This Forum also represents a great opportunity to network with oil and gas industry professionals in an engaging and dynamic environment.

Registration begins at 8:00 AM.  Breakfast and Lunch are included.  8 Professional Development Hours will be awarded by the SPE GCS for participation and completion of the Forum.

There will be a limited number of student-priced attendances available.  These must be requested in advance by email to






Session I – Shale Oil/Gas

I. Uncertainty Analysis and Assisted History Matching Workflow in Shale Oil Reservoirs by Zheng Zhang, BHP Billiton

II. Unconventional reservoir EUR Determination and Well Optimization using Reservoir Simulation by Jim Erdle, CMG

III. Reservoir Engineering Applications of Microseismic Data – Proppant Placement, Permeability Calculation, and Correlation to Production by Sudhendu Kashikar, MicroSeismic Inc.

Session II – Technology & Innovation

I. Robust Reduced Complexity Modeling in Reservoir Simulation and Optimization by Eduardo Gildin, Texas A & M

II. A Novel Dynamic Gridding based Approach in Reservoir Simulations with Complex Flow Processes and Rock Heterogeneity by Hussain Hoteit, Chevron

III. Data Mining for Innovative Reservoir Management by Sebastien Matringe & David Castineira, QRI

Keynote Luncheon

I. Characterizing Shale Plays – The Importance of Recognizing What You Don’t Know by Brad Berg, Anadarko

Session III – EOR/IOR

I. Application of the Pulser method to extend or modify CO2 performance prototypes by Shunhua Liu, Oxy

II. Booking EOR/IOR Reserves by Marylena Garcia, Ryder Scott

Session IV – Field Optimization

I. Applications of Field Development Optimization Technology in Gulf of Mexico Oil Fields by Michael Litvak, BP

II. Derivative-Free Optimization for Generalized Oil Field Development with Constraints by Obi Isebor, BP

Student Posters

I. Development of Laboratory Scale Testing for an In-Situ Thermal Conductivity Probe by Analicia Caylor, University of Houston

II. Production Forecasting using Type Wells, Diagnostic Analysis and Hybrid Models by Ayush Rastogi, University of Houston

III. Identifying the Ideal Stimulation Design Via Multivariate Analysis by Ryan Rice, Texas A & M University

IV. Mechanism of Oil In-Situ Upgrading during In-Situ Combustion through Oil Fractionation by Assiya Suleimenova , Texas A & M University

V. Evaluation of Betaine:Anionic surfactant Ratio for foam in Presence of Crude Oil by Aarthi Muthuswamy , Rice University

VI. Effect of surfactant partitioning on foam transport in porous media by Yongchao Zeng, Rice University

VII. Fracture Network Modeling and Simulation by Mahmood Shakiba, University of Texas at Austin

VIII. Experimental Study of Convective Dissolution of Carbon Dioxide in Porous Media by Yu Liang, University of Texas at Austin




Session I – Shale Reservoirs

I. Uncertainty Analysis and Assisted History Matching Workflow in Shale Oil Reservoirs by Zheng, Zhang, BHP Billiton

For newly developed shale oil reservoirs, it is a challenging task to arrive at reasonable long-term production forecasts due to both large uncertainties associated with reservoir parameters and short production history. Assisted history matching plays an important role in integrating key uncertainties in order to arrive at a calibrated production prediction.

In this talk, we present two workflows to utilize a stochastic history matching method to a multi-fracture horizontal well in Eagle Ford shale oil reservoir. First, we discuss the impact of reservoir properties, hydraulic fractures, microfracs, phase behavior and rock characteristics on production behavior using sensitivity analysis. Next, we use the key uncertainties to calibrate the model against historical data using genetic algorithms. Three different geo-models were considered in all cases. However, in one workflow, they were evolved separately while in another one, they were evolved as a group. Production forecasting based on updated models from both workflows were categorized into several groups using cluster analysis. Then, the suggested workflows were compared according to their advantages and limitations. The results indicated that for workflow I, inaccuracy in uncertainty ranges could results in an incomplete set of updated models during evolution. For workflow II, reasonable probability must be provided; otherwise good model for certain geo-models may be ignored because the results could be constrained by less-probable geo-models. For unconventional reservoirs with very short limited static and dynamic data, our proposed workflows provide a flexible framework for capturing key uncertainties.  Thus, they can be applied flexibly for long-term production forecasting or for identifying key areas for further data acquisition.

II. Unconventional Reservoir EUR Calculation & Well Optimization using Reservoir Simulation by Jim Erdle, CMG

The use of reservoir simulation (i.e. numerical simulation) to history-match and optimize production from hydraulically fractured horizontal wells completed in “unconventional” (e.g. shale gas, shale liquids & other “tight oil”) reservoirs has evolved in terms of the physics that can be incorporated and the workflows that can be applied to make “physics-based” calculations of a range of plausible EURs (from one or more wells) and optimization of well configuration and density/spacing possible in reasonable time frames (e.g. hours or a few days).  This talk presents the application of reservoir simulation and productivity enhancement tools to determine a physics-based range of oil and gas EUR’s and optimum well configurations/spacing for an Eagle Ford “oil window” well.

III. Reservoir engineering applications of microseismic data – proppant placement, permeability calculation, and correlation to production by Sudhendu Kashikar, MicroSeismic Inc.

Presentation abstract: Microseismic monitoring of hydraulic fracturing treatments allows operators to optimize wellbore completions through understanding the response of the reservoir and correlating it to the stimulation and the resulting hydrocarbon production. Analyzing fracture growth with injected fluid volumes helps engineers to improve production and effectively stimulate the entire lateral. Distance-based material balance proppant filling of a discrete fracture network on a stage-by-stage basis enables the operator to distinguish between the total stimulated rock volume (SRV) where microseismic activity was observed and the part that contains proppant filled fractures and will therefore be productive in the long term. A direct correlation between SRV and production can be used to identify the wellbore’s potential after the hydraulic stimulation. Proppant placement analysis in a wellbore-centric coordinate system allows for determination of optimum wellbore spacing, optimum stage length and spacing, and landing depth optimization by evaluating propped height growth above and below the wellbore. Fracture surface generation throughout the treatment can be monitored to identify points of diminished returns and treatment efficiency reductions. Additionally, permeability can be calculated from the spatio-temporal evolution of microseismic events which provides an important input for reservoir simulation.


Session II – Technology & Innovation in Reservoir Engineering

I. Robust Reduced Complexity Modeling in Reservoir Simulation and Optimization by Eduardo Gildin, Texas A & M

Despite great advances in reservoir simulation capabilities with the introduction of high-performance computing (HPC) platforms and enhanced solvers, high fidelity grid-based simulation still remains a challenging task. This task is especially demanding for fine-resolved geological reservoirs with multiphase and multi components and in production optimization and uncertainty quantification frameworks where several calls of the large scale simulation model need to be performed. In order to overcome the computational costs associated with these large-scale models, several forms of model-order reduction have been   proposed in the literature. In porous media flow, two different approaches are used: (1) a ``coarsening" of the discretization grid in a process called upscaling and multiscale methods; and (2) a reduction in the number of state variables (i.e., pressure and  saturations) and parameters directly in a process called approximation of dynamical systems and reparameterization. Recently, the idea of combining both approaches have been proposed using the multiscale model reduction techniques.

In this talk, I will describe the model reduction methods in a systems framework and will show their applicability to mitigate the computational cost in optimization and uncertainty quantification. I will start with linear model reduction and extend the results to nonlinear cases. In particular, I will show results regarding the POD-DEIM method and bilinear approximations. Also, I will show how one can introduce multiscale methods in the reduction framework. I will end the talk with ideas for parameter model reduction (re-parameterization) using high-order singular value decompositions (HOSVD).


II. A Novel Dynamic Gridding based Approach in Reservoir Simulations with Complex Flow Processes and Rock Heterogeneity by Hussain Hoteit, Chevron

Many IOR/EOR recovery processes such as chemical, miscible and steam flooding are often associated with complex flow mechanisms that manifest at the displacement front. Viscous fingering, polymer/surfactant dilution and mixing effects are some of these mechanisms. Accurate modeling of these phenomena requires simulations on high resolution grids to properly capture the physics in the vicinity of the displacement front. Unfortunately high grid resolutions incur longer simulation times. Thus, past efforts at running full-field gas or Chemical EOR simulations were frequently deemed impractical.

The advancement in computational power from software, hardware and parallelism has indeed pushed the limits towards higher resolution simulations. However, this may not be practical in workflows that require simulations on many models to manage uncertainties. Dynamic gridding is one approach that attempts to adjust the grid resolution as needed during the run time. No a priori knowledge is assumed regarding the fluid flow pathways. The simulator can track the location of the displacement front, refine the neighborhood cells, and later coarsen them back as the front progresses. The advantage is reducing the number of grid-blocks, and therefore the computational time, compared to the fully refined grid, while preserving the fluid-flow physics. Although this technology is not new in reservoir simulation, there are persisting challenges in the existing methods related to the computational overhead associated with cell re-mapping, transmissibility re-calculation, and grid up-scaling and down-scaling.

A novel dynamic gridding approach has successfully been implemented into our in-house simulator. The key achievements are: 1) eliminate grid re-mapping and transmissibility re-calculation at the run time, 2) capture heterogeneity associated with all levels of grid refinements, 3) model complex geology with non-uniform gridding, and 4) track multiple fronts associated with surfactant/polymer and chase water slugs. We discuss how we overcame the bottlenecks to leverage this technology from prototypes to complex cases. We also demonstrate our method on prototypes and pilot cases under CEOR recovery processes.

III. Data Mining for Innovative Reservoir Management” by David Castiñeira and Sébastien Matringe, Quantum Reservoir Impact

In a variety of engineering disciplines, data science is starting to displace classical engineering paradigms. In fields as varied as Quantitative Finance, Image Recognition, Social Networks or Drug Design, data-driven models have been developed and used successfully where more classical physics-based models have failed. This presentation will offer examples of how data mining techniques have been used successfully in the context of conventional and unconventional reservoir management. The purpose of the presentation will be to generate thoughts on the future of these technologies in our industry.

In conventional reservoirs, we present a structured and systematic analysis of reservoir data designed to identify key recovery obstacles (the reservoir's issues) and field development opportunities (actions that could improve oil recovery). This diagnosis of the reservoir data is performed at various scales (field, block, layer, well-group, well) to answer a series of key questions designed to provide a thorough understanding of the reservoir's inherent characteristics, its history, and its current performance. Diagnostic questions are answered using a series of metrics, plots, maps, and calculations.

For unconventional plays, we will show how data mining algorithms can be used to evaluate the potential of an acreage and to help the planning of its development. Optimal well designs and locations are determined for a given area and their future performance is estimated to establish the commerciality of a given position. The analytics involved include automated decline-curve and type-curve analysis, clustering algorithms, geostatistical estimation techniques, machine learning and regression models and discriminant analysis. Insights gained by this approach include a fundamental understanding of the inherent potential of a given well location as well as the learning curve to be expected by the introduction of new drilling and completion technologies.

Keynote Luncheon

Characterizing Shale Plays – The Importance of Recognizing What You Don’t Know by Brad Berg, Anadarko

Shale plays typically exhibit much more uncertainty in individual well performance than conventional reservoirs. Understanding this uncertainty is particularly critical during the exploration drilling program when one has relatively few wells on which to base decisions. A systematic approach to understanding and managing this uncertainty can be used to address key questions during the early phases of a drilling program, including "how many wells do I need to drill before I have confidence in the results?" and "does the well performance I’ve seen to date provide the encouragement needed to keep drilling?" To answer these questions, one must quantify the uncertainty surrounding individual well results. The primary take-away from this presentation is that it is critical to recognize, and properly characterize, uncertainty in shale well production performance when planning an exploration drilling program in shale plays. Without such an approach, the commercial potential of a play may not be adequately characterized, leaving the decision-makers without the information needed to determine the path forward. Understanding the uncertainty in well performance, and planning for it, will lead to more efficient exploration activity, and better informed decision-making.


Session III – EOR/IOR

I. A Novel Method of Forecasting CO2 Flood Performance for Various WAG Injection Schemes by Analyzing Injection Pulses by Shunhua Liu, Oxy

A new method for generating dimensionless CO2 flood forecast prototypes for various injection schemes from an existing prototype is presented. Given an existing prototype, the method described in this paper can predict an appropriate prototype for an alternate injection schedule.  This methodology enables engineers to quickly generate forecast prototypes by analyzing the oil/gas production response from breaking down existing prototypes (developed either from simulation or from analog field performance) into injected CO2 pulses. A Microsoft Excel with VBA program (PULSER) based on this methodology has been successfully utilized to generate the prototypes for the fluid streams forecast of several West Texas/New Mexico CO2 flooding projects. When compared to reservoir simulation, the Pulse method is much faster in generating new prototypes and more flexible in accommodating different injection scenarios. The results from this method, full field simulation, and blind testing actual field data are remarkably consistent. 

II. Booking EOR/IOR Reserves by Marylena Garcia, Ryder Scott

Reserves estimation represents the most essential task in the petroleum industry either for internal or external reporting requirements.  Enhance oil recovery (EOR) and improved oil recovery (IOR) processes attempt to recover additional volumes of oil beyond primary recovery. They are usually very complex and demand deep technical knowledge and extensive operational experience for maximizing oil recovery and delivering profitable projects. When estimating and classifying undeveloped reserves for these processes, reserves estimators should do it in accordance with the established standards and regulatory definitions. This presentation will outline some of the key parameters that need to be taken into account during the reserves estimation from EOR/IOR processes according to the SEC rules and the PRMS guideline document, starting with the definition of reserves and their classification based on the range of uncertainty, importance of pilot projects, use of analogies, reliable technology and some other challenges that can impact EOR/IOR reserves.


Session IV – Field Optimization

I. Field Development Optimization with Subsurface Uncertainties in Gulf of Mexico Oil Fields by Michael Litvak, BP

We present a framework and algorithms for the field development optimization with subsurface uncertainties. We applied the developed optimization technology in many oil and gas fields. Thousand potential field development options were evaluated and an optimized development plan was selected. Impacts of subsurface uncertainties on field development decisions were estimated.

We optimize the number of production or injection wells, their locations, perforation intervals, drilling schedules, well rates, etc. As a novel approach, we include additional categorical variables such as depletion strategy, well pattern, or facility size in the optimization process. We consider a limited number of discrete scenarios for each categorical variable (e.g., primary depletion, gas injection, or water injection as three development scenarios).

Field development constraints on well locations, rig schedules, economic risks etc. are incorporated in the optimization. Hydrocarbon recovery or some other economic indicator can be used as the objective function for the optimization and applied for ranking the field development options. Subsurface uncertainties are represented by incorporating multiple reservoir models in the optimization process. Our results indicate that the proposed algorithms can effectively handle field development optimization problems with many reservoir models representing subsurface uncertainties.

As examples, we demonstrate the applications of the developed field development optimization technology in two Gulf of Mexico oil fields. We optimized well placement, drilling schedule, well production/injection rates, perforation strategy, injection strategy, and facility modifications in these fields. We demonstrated advantageous features of our recommendations considering subsurface uncertainties in reservoir descriptions.


II. Derivative-free Optimization for Generalized Oil Field Development with Constraints by Obi Isebor, BP

The tasks of petroleum field development and reservoir management involve making decisions about the number and type of new wells in a field, the locations of these wells, the order in which they should be drilled, in addition to determining their time-varying controls (rates and pressures). These decisions need to be made while accounting for economic, drilling, facility and other production constraints. A derivative-free framework will be presented that approaches the optimization of field development and reservoir management decisions, while satisfying the specified constraints.




Miles Palke

Date and Time

Thu, May 8, 2014

8 a.m. - 4 p.m.
(GMT-0500) America/Chicago

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Anadarko Tower

1201 Lake Robbins Drive
The Woodlands, Texas 77380
United States

Group(s): Reservoir