bluuebunny/crossref_metadata_2025 huggingface dataset:
Ensemble Model Predictive Control for Robust Automated Managed Pressure Drilling
Authors: Eaton Ammon N. , Beal Logan D. , Thorpe Sam D. , Janis Ethan H. , Hubbell Casey , Hedengren John D. , Nybø Roar , Aghito Manuel , Bjørkevoll Knut , Boubsi Rachid El , Braaksma Jelmer , van Og Geertjan
Published: September 2015

For automation of managed pressure drilling (MPD) to succeed, the automation system needs access to accurate measurement data and the ability to translate this into a correct representation of reality in the well. However, inaccuracies due to calibration problems and errors or omissions in manually entered data may combine with spurious behavior in the control model to make the automation system unreliable. This study presents a novel control scheme for automated MPD that addresses the problem of model reliability by using multiple control models to provide optimal control moves, even with the failure of one or two models.

This work simulates the ability to optimize MPD operations with realistic measurement signals using Model Predictive Control (MPC) with a range of model types. Further, it provides a robust automated MPD system to reduce interruptions to drilling operations. This work makes use of a high fidelity dynamic well bore model in addition to low order and empirical control models. The three controllers feed into a switch that selects the best available controller recommendation and allows for a seamless transition between controllers. The switch control scheme enables automated switching between the controllers in the event of one or two models failing and also allows for the tuning and troubleshooting of one model while the others continue to run, all without any interruption to the drilling process.

One of the key innovations in the ensemble switch is the seamless transition between controllers. This is accomplished by using the current process manipulated variable values as initial values for the optimization routines in the model predictive controllers that are not actively used to control the well. The strategy is tested in common drilling situations.

Two typical drilling scenarios are simulated: normal drilling operations and a pipe connection procedure. The validity of the novel control structure in each scenario is verified through simulated outliers, drift, and noise, as well as simulated controller failure and lack of optimal solution convergence. The controller is able to maintain bit pressure within +/− 1 bar of the 400 bar set point during normal drilling operations despite temporary signal loss and poor data quality. Also, the bit pressure is held within +/− 5 bar of the 340 bar set point during a pipe connection procedure with no bit pressure measurements available to the controller.

The techniques presented here can be used for more robust and stable automated MPD. Moreover, multiple models provide benefits that are typically associated with improved reliability due to hardware and safety systems redundancy allowing drilling to continue with fewer interruptions.
A Comprehensive Study of Proppant Transport in a Hydraulic Fracture
Authors: Blyton Christopher A. , Gala Deepen P. , Sharma Mukul M.
Published: September 2015

The effective placement of proppant in a fracture has a dominant effect on well productivity. Existing hydraulic fracture models simplify proppant transport calculations to varying degrees and are often found to over-predict propped or effective fracture lengths by 100 to 300%. A common assumption is that the average proppant velocity due to flow is equal to the average carrier fluid velocity, while the settling velocity calculation uses Stokes' law. To accurately determine the placement of proppant in a fracture, it is necessary to rigorously account for many effects not included in the above assumptions.

In this study, the motion of particles flowing with a fluid between fracture walls has been simulated using a coupled CFD-DEM code that utilizes both particle dynamics and computational fluid dynamics calculations to rigorously account for both. These simulations determine individual particle trajectories as particle to particle and particle to wall collisions occur and include the effect of fluid flow and gravity. The results show that the proppant concentration and the ratio of proppant diameter to fracture width govern the relative velocity of proppant and fluid. Further, the dependencies of settling velocity on apparent fluid viscosity, proppant diameter and the density difference between the proppant and fluid predicted by Stokes' law were found to apply. However, additional effects have been quantified and shown to substantially alter the predictions from Stokes' law. Proppant concentration and slot flow Reynold's number were both shown to modify the settling velocity predicted by Stokes' law, as does the ratio of proppant diameter to slot width. The effect of leak-off was found to be negligible in terms of altering either the settling velocity or the relative velocity of proppant and fluid. The models developed from the direct numerical simulations have been incorporated into an existing fully 3-D hydraulic fracturing simulator. This simulator couples fracture geomechanics with fluid flow and proppant transport considerations to enable the fracture geometry and proppant distribution to be determined. Unlike all previous studies, these effects are included together and so are shown to be inter-dependent, allowing us for the first time to accurately model proppant transport.

As noted above, proppant velocities have been accurately determined without simplifying approximations and with all relevant effects included, showing inter-dependence between the different effects. Two engineering fracture design parameters, injection rate and fluid rheology, have been varied to show the effect on proppant placement in a typical shale reservoir. This allows for an understanding of the relative importance of each and optimization of the treatment to a particular application.
Topological Data Analysis to Solve Big Data Problem in Reservoir Engineering: Application to Inverted 4D Seismic Data
Authors: Alfaleh Abdulhamed , Wang Yuhe , Yan Bicheng , Killough John , Song Hongqing , Wei Chenji
Published: September 2015

Data analysis is one of the most important topics in any industry. In petroleum engineering, the large, complex, and multi-dimensional reservoir data sets (big data) presents a challenge for engineers to study the masses of unstructured information and make decisions. A new approach to analyze complex data is called Topological Data Analysis (TDA) which aims to extract meaningful information from such data. TDA relies on the concept that complex data has shapes where shape has meanings. It analyzes the shape of complex data, identifying clusters and their statistical significance. The objective of this paper is to introduce TDA to reservoir engineering using an example of inverted 4D seismic data for studying reservoir connectivity and compartmentalization.

In this paper, we introduce the principles of TDA and discuss its potential in reservoir engineering, which could allow identification of reservoir engineering data behavior, recognition of new opportunities, detection of anomalies and events, and minimizing uncertainties. The TDA procedures are introduced using inverted 4D seismic data set to study reservoir connectivity and compartmentalization. The process to generate and process the data set is explained. Similarity distance function and lenses are defined and used to create TDA graphs for feature identification and analysis.

It is shown that TDA is able to predict the compartmentaliztion of the reservoir models with various process configurations. Variance normalized Euclidean and topological neighborhood function are used successfully to compartmentalize the reservoir model. Using normalized input dataset, correlation and principle component analysis also create similar compartments. The success of TDA in discovering meaningful patterns is attributed to the similarity distance function representing the objective of study, one or more lenses exposing the data, and the right combination of input data, similarity distance function and lenses.

A promising big data analysis method, TDA, is introduced to reservoir engineering application with principles, procedures and examples. It has been shown that TDA can automatically discover critical intelligence within the 4D seismic data set for studying reservoir connectivity and compartmentalization, which are essential to the accuracy of forecasts and development plans, the validity of reservoir simulation, and the success of performance diagnostics and optimization.
Operational Risk: Stepping Beyond Bow-Ties
Authors: Fraser D. , Braun J. , Cunningham M. , Marple B. , Moore D. D. , Sas-Jaworsky A. , Wilson J.
Published: September 2015

This paper presents the Multiple Physical Barrier (MPB) approach to operational (or process) risk that was developed by Argonne National Laboratory with the help of key collaborators from the upstream oil and gas industry.

This approach begins with a comparison to the “bow-tie” technique that is commonly used in the industry for identifying operational (or process) risk. Bow-ties identify a variety of different types of barriers and help communicate safety principles that link causal factors and subsequent actions to a specific event – such as a loss of well control event. Bow-tie barriers commonly include equipment, processes, plans, methods, and sometimes people, with their know-how and experience.

However, by narrowing the focus to physical barriers (e.g. the source of operational risk) as is consistent with the NORSOK D-010 Standard, and by developing “success paths” that enable each barrier to perform its “safety function,” the MPB approach is demonstrated to step further toward a systematic approach to operational risk management. Additionally, this approach can be seen to help guide operational process design as well as lifecycle management.

To demonstrate the versatility of the MPB approach, several applications have been selected that highlight how operational risk can be systematically identified and communicated in different offshore applications: Drilling, including an operational risk comparison between conventional drilling and Managed Pressure Drilling; Completions, installing and de-installing production packers, a barrier that highlights the life-cycle aspect of the MPB approach; and Coiled Tubing Workovers, a risk based design diagram for ensuring that barriers are in place to ensure a successful operation.

By making a clear distinction between physical barriers and success path components (including human actions, processes, and plans), the MPB approach adds systematic clarification for understanding and characterizing risks in a direct manner. The MPB approach also enables the identification of chains of causality and builds important steps toward enabling risk quantification. As such, the MPB approach provides a direct and systematic approach for assessing and communicating risk, as well as helping operations to be successful.
Production Analysis Of One Shale Gas Reservoir In China
Authors: Pang Wei , Wu Qiong , He Ying , Du Juan , Zhang Tongyi , Ehlig-Economides Christine A.
Published: September 2015

China is the third country gaining shale gas breakthrough in the world after the United States and Canada. Fuling shale gas play is the most successful shale gas reservoir by far in China with average absolute open flow (AOF) capacity of 400 thousand cubic meters per well and the amount reaches 8 million cubic meters in one year for some wells. However, production rate and pressure transient analysis show quite different flow regimes which may mean that the wells are producing quite differently and stimulating treatment have distinct performance.

In this paper, 20 wells in the shale gas reservoir in China are analyzed with production rate and pressure transient data using rate normalized pressure (RNP) and rate normalized pressure derivative (RNP') method. The 20 wells log-log interpretation plots are drawn to diagnose the flow regimes and flow regime sequence. In the log-log plots, nearly half of the wells see unit slop, which indicate boundary dominated flow within 1 to 2 years, so the stimulated reservoir volume (SRV) is very small. From production rate data, most producing wells see interference when their neighborhood wells are hydraulic fracturing. Interference imposes great effect on log-log interpretation plots, so interpretation comparison are made with and without using rate and pressure transient data after interference. Because of interference, liquid loading may be a problem for some wells. External source of energy supplying for the shale gas layer is researched by rate and pressure transient interpretation modeling because some wells see external pressure supply.

Results show that Fuling shale gas reservoir has unique production performance with production rate keeping nearly constant within 2 years for some wells. Fuling Phase I shale gas wells exhibit 4 different flow regimes with relevant interpretation models categorized by homogenous formation with boundary, dual porosity formation, pressure support, severe liquid producing. Interference with adjacent wells during hydraulic fracturing may occur and cause great effect on rate and pressure transient data interpretation.

To the author's knowledge, this is the first time that comprehensively describes shale gas production performance detail in China. 4 different flow regimes with different formation interpretation models within one reservoir block are rarely reported.
East Vacuum Grayburg San Andres Unit, 30 Years of CO2 Flooding: Accomplishments, Challenges and Opportunities
Authors: Moffitt Paul , Pecore Doug , Trees Mark , Salts Greg
Published: September 2015

The East Vacuum Grayburg San Andres Unit (EVGSAU) began CO2 injection in 1985 which is still continuing today. Performance of CO2 injection has been very favorable in the main pay zone with oil recoveries exceeding 45 percent overall in the unit. Technical challenges remain in maintaining and increasing oil recovery but future opportunities also exist such as CO2 flooding the transition/residual oil zone and improving overall volumetric sweep efficiency.

Waterflooding in EVGSAU began in 1980 to pressure up the reservoir in preparation for CO2 flooding. CO2 injection began in 1985 with a 2:1 water: alternating gas (WAG) ratio to help provide conformance control. Overall oil recovery from the main portion of the reservoir in the CO2 flooding area alone is above 55 % OOIP. Approximately 12.5 % OOIP recovery has been due to CO2 flooding. This paper will detail some of the challenges experienced during CO2 flooding which include maintaining reservoir pressure above minimum miscibility pressure, maintaining high injection rates in WAG injection wells, and handling large volumes of produced gas. During the early 2000's, 19 laterals were drilled out of existing vertical wells which assisted the unit in increasing oil production rates and reducing gas coning.

Future opportunities include expansion of the current gas plant to handle the increasing gas-oil ratio of produced oil as the CO2 flood continues to mature, CO2 flooding the transition/residual oil zone, infill drilling in the main pay zone to increase areal sweep efficiency and polymer gel treatments to address poor vertical conformance. A foam treatment is also being planned for EVGSAU.

CO2 flooding in the EVGSAU has been impacted during its history by changing oil and CO2 prices and many technical challenges. Despite these challenges, oil recovery by CO2 flooding has been favorable and further steps are being taken to increase long-term oil recovery.
Development of a General Likelihood Maximization Method for Robust Parameter Estimation from Pressure Transient Data
Authors: Bandyopadhyay Parag , Horne Roland N.
Published: September 2015

Conventional parameter estimation methods used in pressure transient analysis assume specific analytical probability distributions of error and use corresponding likelihood functions to estimate the key parameters such as reservoir permeability, wellbore storage, skin and distance to reservoir boundary. The most commonly used method is that of least squares, which assumes that measurement errors follow a Gaussian distribution. Lack of a priori information regarding the nature of these measurement errors have made the ordinary least squares method (OLS) the most common method for parameter estimation.

In this study, on examination of multiple field data sets, the assumption of Gaussian errors was found to be weak. The use of OLS in such cases leads to biased estimates of reservoir parameters. A general method based on the framework of likelihood maximization that is inherent to parameter estimation was developed. This method does not require a prior assumption regarding the nature of the distribution of the underlying errors in the data. A hybrid variation was developed to improve the convergence properties of this method. The accuracy and convergence properties of this method were compared to that of OLS for field and synthetic datasets and found to be superior to OLS for arbitrary error distributions, while still maintaining accuracy in cases with truly Gaussian errors.
The Reservoir Technical Limits Approach Applied to Maximising Recovery from Volumetric and Aquifer-drive Gas Fields
Authors: Alane Abdelmadjid , Lumsden Peter J. , Smalley P. Craig , Hallam Richard , Salino Peter A. , Wells Steven J. , Primmer Tim J.
Published: September 2015

Maximising recovery of hydrocarbons from oil and gas fields represents responsible asset management and is extremely valuable both to the operator and the host country. Doing this successfully involves a complex combination of technical, commercial, organizational and human factors. This was addressed by developing the Reservoir Technical Limits (RTL™) process; the process and its application to oil fields was described in a 2009 SPE paper (109555). The present paper describes subsequent progress in developing RTL™, including a description of a new gas efficiency factor framework for use in volumetric and aquifer-drive reservoirs.
Transient Flowing-Fluid Temperature Modeling in Oil Reservoirs for Flow Associated with Large Drawdowns
Authors: Chevarunotai N. , Hasan A. R. , Kabir C. S.
Published: September 2015

Modern downhole temperature measurements indicate that bottomhole fluid temperature could be significantly higher or lower than the original reservoir temperature, especially in reservoirs where high pressure drawdown is expected during production. This recent finding contradicts the isothermal assumption originally made for routine caluculations. In a high-pressure drawdown environment, the Joule-Thomson (J-T) phenomenon plays an important role in fluid temperature alteration in the reservoir.

This paper presents a robust analytical model to estimate the flowing-fluid temperature distribution in a reservoir that accounts for the J-T heating or cooling effect. All significant heat-transfer mechanisms for fluid flow in the reservoir, including heat transfer due to convection, J-T phenomenon, and heat transfer from over- and under-burden formations, are incorporated in this study. The proposed model is successfully validated with results from a rigorous numerical simulator using field data.
Modeling Method of Point Bar Internal Architecture of Meandering River Reservoir Based on Meander Migration Process Inversion Algorithm and Virtual Geo-surfaces Automatic Fitting Technology
Authors: Shu Xiao , Hu Yong , Jin Baoqiang , Dong Rencheng , Zhou Haiyan , Wang Jijun
Published: September 2015

This paper developed a new modeling method of the point bar internal architecture of meandering river reservoir based on a new meander migration process inversion algorithm and virtual geo-surfaces automatic fitting technology. Firstly, we proposed a new inversion simulation algorithm of meander migration process based on the river hydrodynamics which can backward simulate the migration centerlines of channel at each time step starting from the centerline of the terminal abandoned channel. Considering the fact that lateral accretion interlayers have the same strike with the ancient channel where they draped, through extracting restored channel centerlines at very high density combined with 3D geometry characterization, the 3D virtual geo-surfaces of lateral accretion interlayers inside the point bar were built. After examining the extent of match-up between each virtual geo-surfaces and well data in 3D space, the virtual geo-surfaces which don't conform to well data were removed and the virtual geo-surfaces which conform to both well data and predefined rules were retained as the final geo-surfaces model of real lateral accretion interlayers. In order to test the reliability of this method, we applied this method to study the reservoir architecture of the west region of NmI3 depositional unit of M oilfield in Bohai Bay Basin, East China.

M oilfield is one of the biggest oilfield in Bohai Bay Basin, East China. A total of 140 wells penetrated the NmI3 depositional unit. The average well spacing is about 400m. Constrained by the well spacing and the size of the channel and the point bar, we only characterize the 4th order architecture unit by means of interwell 3D correlation. And the lateral accretion interlayers model was built using the inversion simulation method.

Based on the internal architecture model of the point bar, we explained the big difference of fluid production rate between 2 prodution wells which are in the same point bar and have roughly the same distance to the injection water well. The results show that the the injected water flows along the strike of lateral accretion interlayers, suggesting an advantageous flow path was formed inside the point bar. So the relative spatial position between wells and lateral accretion interlayers is a key factor that influences production dynamic. This rule can be used to guide well placement and waterflooding optimization in point bar reservoir.
Dimensionality-Dependent Foam Rheological Properties: How to Go From Linear to Radial Geometry for Foam Modeling and Simulation?
Authors: Lee Woochan , Lee Seungjun , Kam Seung Ihl
Published: September 2015

Numerous laboratory and field tests reveal that foam can effectively control gas mobility and improve sweep efficiency, if correctly designed. It is believed that there is a significant gap between small laboratory-scale experiments and large field-scale tests because of two main reasons: (i) typical laboratory flow tests are conducted in linear systems, while field-scale foam EOR processes are performed in radial (or spherical partly) systems; and (ii) through the complicated in-situ lamella creation/coalescence mechanisms and non-Newtonian behavior, foam rheology depends on the geometry and dimensionality. As a result, it is still an open question how to translate laboratory-measured data to field-scale treatments.

For the first time, this study investigates how foam rheological properties change depending on the dimensionality and how such dimensionality-dependent properties are affected by different flowing conditions, by using mechanistic foam fractional flow analysis. Complex foam flow characteristics such as three foam states (weak-foam, strong-foam, and intermediate states) and two steady-state strong-foam regimes (high-quality regime and low-quality regime) lie in the heart of this analysis.

The calculation results from a small radial and spherical system showed that (i) for strong foams in the low-quality regime injected, foam mobility decreased (or mobility reduction factor increased) significantly with distance which improved sweep efficiency; (ii) for strong foams in the high-quality regime, the situation became more complicated – near the well foam mobility decreased, but away from the well foam mobility increased with distance, which eventually gave lower sweep efficiency; and (iii) for weak foams injected, foam mobility increased with distance which lowered sweep efficiency. The results implied that the use of fixed value of mobility reduction factor, which is a common practice in field-scale reservoir simulations, might lead to a significant error. When the method was applied to a larger scale, it was shown that strong foams could propagate deeper into the reservoir at higher injection rate, higher injection pressure, and at lower injection foam quality. Foam propagation distance was very sensitive to these injection conditions for strong foams in the high-quality regime, but much less sensitive for strong foams in the low-quality regime.
Water Content Effects on Dynamic Elastic Properties of Organic-rich Shale
Authors: Lai B.T. , Li H. , Zhang J.L. , Jacobi D. , Georgi D.
Published: September 2015

Acoustic velocity measurements are an important non-destructive way to investigate dynamic rock mechanical properties. Water content and bedding-plane-induced anisotropy are reported to significantly impact the acoustic velocities of siliciclastic sandstones and laminated carbonates. This relationship in organic-rich shales however is not well understood and has yet to be investigated. The mechanical properties of organic-rich shales are affected by changes in water content, laminations, total organic content (TOC), and microstructures. In particular, kerogen density that accompanies changes in the composition of the TOC during maturity can significantly influence the acoustic responses within source rocks.

To understand how these variables influence acoustic responses in organic shales, two sets of cores from the Eagle Ford shale were investigated: one set cut parallel to bedding and the other perpendicular to bedding. Textures of the samples from each set were characterized using CT scanning. NMR was used to measure the water content, and XRD to analyze the mineralogy. Scanning Electron Microscope (SEM) was also used to characterize the microstucture. Acoustic velocity measurements were then made on each set at various confining pressures using the ultrasonic pulse transmission technique.

The results show that confining pressure, water content and laminations have significant impact on both compressional (P-wave) and shear wave (S-wave) velocity. Both velocities increase as confining pressure increases. Velocities measured from cores cut parallel to bedding are on average 20% higher than those cut perpendicular to bedding. Increasing water content decreases both velocities. The impact of water content on shear velocity was found to be significant compared to the response with compressional velocity. As a result, the water content was found to lower both Young's modulus and shear modulus, which is opposite to the reported results in conventional reservoir lithology. In addition, both P- and S- wave velocities show a linear decrease as TOC increases, and also they both decrease with increasing of clay content. The mechanisms that lead to water content alternation of rock mechanical properties might be a combined result of the clay-water interaction, the chemical reaction, and the capillary pressure changes.
Machine Learning Applied to Multiwell Test Analysis and Flow Rate Reconstruction
Authors: Tian Chuan , Horne Roland N.
Published: September 2015

Permanent downhole gauges (PDGs) can provide a continuous record of flow rate and pressure, which provides us rich information about the reservoir and makes PDG data a valuable source for reservoir analysis. In previous work, it has been shown that kernel ridge regression based machine learning is a promising tool to interpret pressure transients from a single PDG. Kernel ridge regression denoises and deconvolves the pressure signal efficiently and recovers the full reservoir behaviors. In this work, the machine learning framework was extended to two applications: multiwell testing and flow rate reconstruction.

The multiwell testing was formulated into the machine learning algorithm using a feature-coefficient-target model. The features were nonlinear functions of flow rate histories of all the wells. For each well, the features and the pressure target of this well were used to train its coefficients, which implicitly contain the information about the reservoir model and well interactions. The reservoir model can then be revealed by predicting the pressure corresponding to a simple rate history with the trained model. The multiwell machine learning model was demonstrated to be useful in several different ways, including artificial interference testing and reservoir pressure prediction at various well locations based on flow rate data only.

Flow rate reconstruction aims at estimating any missing flow rate history by using available pressure history. This is a very useful capability in practical applications in which individual well rates are not recorded continuously. A set of new features were developed as functions of pressure to model the flow rate. Coupled with kernel ridge regression, the developed features were tested on both synthetic and real data sets and demonstrated high prediction accuracy. The success of the rate reconstruction modeling also illustrates the flexibility of machine learning to different kinds of modeling, by adapting features and targets.

The models for both applications maintained the advantages of the machine learning based single well pressure interpretation in terms of the accuracy of prediction, computational efficiency and tolerance to noise. This work further demonstrates machine learning as a promising technique for PDG data analysis.
Challenges in Deepwater Reservoir Characterization: From Well Log Interpretation and Well Testing to 3D Geocellular Modeling
Authors: Saikia Kalyan , Khan Waqar , Ramakrishnan Sandeep
Published: September 2015

With recent global success in many frontier deepwater prospects, these areas gained sudden attention (specifically presalt) amongst deepwater operators. As a result, exploration activities have increased many folds. Consequently, operators are trying to characterize these prospects as quickly as possible to maximize their return on investment (ROI). Such objectives can only be achieved if reservoir characterization of these blocks is performed with minimum error and uncertainty. To overcome these challenges, this paper presents an integrated approach wherein a combination of data aids constructing a representative initial three-dimensional (3D) geocellular model. This model is then fine-tuned and conditioned with the incorporation of well testing results from offset wells (if any). These preliminary models are created based on certain assumption and they need constant updates throughout the cycle of project to validate the theoretical assumptions combing detailed log interpretation and well testing data. A case study is presented to demonstrate the workflow and highlight the benefits of this technique.
Characterizing Reservoir Thermofacies by Using Distributed Temperature Sensing Measurements
Authors: Leone A. , Galli G. , Drago A. , Brown G.
Published: September 2015

A new distributed temperature sensing (DTS) interpretation method used in horizontal injection wells characterizes static facies and highlights independent features called thermofacies. This method allows for evaluating the intrinsic dynamic responses of reservoir layers using a time-lapse approach, taking into account the skin changes during the well's life and the possible activation of structural elements such as faults and fractures.

Based on DTS temperature analysis acquired during a well's shut-in period, this interpretation method provides two major results: the thermal performance indicator (TPI) and the thermofacies. The TPI adds a dynamic component to the static image of a well provided by openhole logs, even taking into account structural elements and inflow performance evaluation. When DTS inflow contributions are available, some independent facies (thermofacies) can be defined, showing the dynamic behavior of a well in a standalone mode.

The main advantage of this interpretation method is to provide a tool capable of driving field development to optimize the production while focusing on those log facies characterized by the dynamic injection performance; not only based on their static petrophysical evaluation.
A New Material Balance Equation for the Analysis of Liquid Rich Natural Gas Reservoirs
Authors: Zhang Miao , Ayala Luis F.
Published: September 2015

In this study, we analytically cross examine the consistency among available zero-dimensional material balance equations (MBEs) for liquid-rich gas equations and derive a new simple yet rigorous MBE starting from governing equations applicable to these systems. We propose a new zero-dimensional (i.e. tank) material balance equation that is directly applicable to the analysis of liquid-rich (wet and retrograde) gas reservoirs by expression of the equations in term of an equivalent gas molar density. Following model development, proposed model predictions of gas reservoir behavior with varying condensate content (lean, intermediate and rich) are investigated and critically compared to previous zero-dimensional models. All models are employed to predict reservoir performance given reservoir original-fluids-in-place and compared against benchmark examples created by numerical simulation. Actual field examples are also analyzed using existing and proposed models to test the ability of the proposed models to provide reliable reserve estimations using straight-line methods. The proposed density-based equation is proven to be straightforward to implement since is written in terms of density. This, in turn, allows it be directly expressed as an extension of the dry gas MBE, while not requiring the implementation of two-phase Z-factors.
Modeling of Compositional Variation with Depth for Five North Sea Reservoirs
Authors: Pedersen Karen Schou , Hjermstad Hans Petter
Published: September 2015

Compositional data for reservoir fluids sampled in various depths in five different North Sea reservoirs is presented and analyzed. All five reservoirs have a gas-oil contact and for each reservoir one composition exists from above and two or more from below the gas-oil contact. For all reservoirs the compositional gradient is higher than can be explained by gravity segregation alone. The highest compositional gradient is seen for a reservoir, which is rich in asphaltenes. The compositional variation with depth is modeled using the Haase model, which expresses the influence of a temperature gradient in terms of the specific enthalpies of each fluid component. The high compositional variation seen for the asphaltenic reservoir fluid suggests that the condensed aromatic compounds, which are the main constituents of asphaltenes, have a significantly higher absolute specific ideal gas enthalpy than paraffinic and naphthenic compounds.
Robust Optimization of Well Placement in Geologically Complex Reservoirs
Authors: Alpak Faruk O. , Jin Long , Ramirez Benjamin A.
Published: September 2015

Optimal decisions about well placement and control in geologically complex reservoirs can reduce subsurface risks associated with field development plans. Especially, in deepwater developments, economic constraints impose stringent limits on the number of wells that can be drilled without compromising economic profitability. Thus, optimal placement and operation of wells often have a significant impact on project rewards. Reservoir management decisions are made based on evaluations of subsurface models that integrate all available data by use of geomodeling and reservoir simulation. It is important to recognize that uncertainty is inherent to reservoir models due to limited data/knowledge about subsurface. A robust reservoir management strategy calls for rigorous techniques that account for stratigraphic and structural uncertainty in geologically complex deepwater reservoirs. A robust well-location optimization method is developed particularly suitable for such reservoirs. A steepest-ascent algorithm that takes advantage of distributed parallel computation constitutes the computational core of the optimization method. The method operates in parallel on a number of probable reservoir models and maximizes the expected value of ultimate recovery factor while delivering minimum variance over the model uncertainty range. A realistic synthetic test case is evaluated for demonstrating the concept. The case uses alternative geologic models of a lobate turbidite reservoir with complex stratigraphic architecture. The results validate the auspicious features of the robust well location optimization method.
A Modified HLD-NAC Equation of State to Predict Alkali-Surfactant-Oil-Brine Phase Behavior
Authors: Ghosh Soumyadeep , Johns Russell Taylor
Published: September 2015

Reservoir crudes often contain acidic components (primarily naphthenic acids), which undergo neutralization to form soaps in the presence of alkali. The generated soaps perform synergistically with injected synthetic surfactants to mobilize waterflood residual oil in what is termed alkali-surfactant-polymer (ASP) flooding. The two main advantages of using alkali in enhanced oil recovery (EOR) are to lower cost by injecting less expensive synthetic surfactant and to reduce adsorption of the surfactant on the mineral surfaces. The addition of alkali, however, complicates the measurement and prediction of the microemulsion phase behavior that forms with acidic crudes. For a robust chemical flood design, a comprehensive understanding of the microemulsion phase behavior in such processes is critical. Chemical flooding simulators currently employ Hand's method to fit a limited amount of measured data, but that approach likely does not adequately predict the phase behavior outside of the range of the measured data.

In this paper, we present a novel and practical alternative by employing a modified HLD-NAC equation of state that is predictive in nature. The HLD-NAC model uses the hydrophyllic-lypophillic difference (HLD) concept along with net and average curvature equations (NAC). The modified HLD-NAC model described by Ghosh and Johns (2014) to model surfactant-polymer (SP) phase behavior is extended here for ASP. We use an empirical equation to calculate the acid distribution coefficient from the molecular structure of the soap. Key HLD-NAC parameters like optimum salinities and optimum solubilization ratios are calculated from soap mole fraction weighted equations. The model is tuned to data from phase behavior experiments with real crudes to demonstrate the procedure. We also examine the ability of the new model to predict fish plots and activity charts that show the evolution of the three-phase region. The predictions of the model are in good agreement with measured data and all data is able to be matched.
A Multi-Objective Markov Chain Monte Carlo Approach for History Matching and Uncertainty Quantification
Authors: Olalotiti-Lawal Feyi , Datta-Gupta Akhil
Published: September 2015

We present a probabilistic approach for integrating multiple data into subsurface flow models. Our approach is based on a Bayesian framework whereby we exhaustively sample the multi-dimensional posterior distribution to define a Pareto front which represents the trade-off between multiple objectives during history matching. These objectives can be matching of water-cut, GOR, BHP and time-lapse seismic data. For field applications, these objectives do not necessarily move in tandem because of measurement errors and also interpretative nature of the seismic data.

Our proposed method is built on a Differential Evolution Markov Chain Monte Carlo (DEMC) algorithm in which multiple Markov Chains are run in parallel. First, a dominance relationship is established amongst multiple models. This is followed by construction of the posterior distribution based on a hypervolume measure. A unique aspect of our method is the proposal selection which is based on a random walk on two arbitrarily selected chains. This promotes effective mixing of the chains resulting in improved convergence.

We illustrate the algorithm using a nine-spot waterflood model whereby we use water-cut and bottomhole flowing pressure data to calibrate the permeability field. The permeability field is reparameterized using a previously proposed Grid Connectivity Transform (GCT) which is defined based on a decomposition of the grid Laplacian. The compression power of GCT allows us to reconstruct the permeability field with few parameters, thus significantly improving the computational efficiency of the MCMC approach. Next, we applied the method to the Brugge benchmark case involving 10 water injectors and 20 producers. For both cases, the algorithm provides an ensemble of models all constrained to the history data and defines a probabilistic Pareto front in the objective space. Several experimental runs were conducted to compare the effectiveness of the algorithm with NonDominated Sorting Genetic Algorithms (NSGA). Higher hypervolume was constantly measured using our algorithm which indicates that more optimal solutions were sampled. Our method provides a novel approach for subsurface model calibration and uncertainty quantification using MCMC in which the communication between parallel Markov chains enhances adequate mixing. This significantly improves the convergence without loss in sampling quality.

We demonstrate that the proposed approach can efficiently integrate seemingly conflicting data into reservoir models while quantifying uncertainties. Besides reservoir model calibration, we expect that the algorithm will find application in probabilistic production optimization problems as well.
Rock Typing of Tight Gas Sands: A Case Study in Lance and Mesaverde Formations From Jonah Field
Authors: Aliyev Elshan , Saidian Milad , Prasad Manika
Published: September 2015

The Jonah field is one of the biggest tight gas sand fields in the Green River basin. Production profiles from its deeper sections show high liquid hydrocarbons close to the Pinedale anticline, especially in Mesaverde and Lance formations. To assess the potential of condensate production, new approaches for rock classification are needed to differentiate between discontinuous sandstone layers and the interbedded siltstones. A gamma ray cut off of 75 API has been defined to distinguish between sandstone and siltstone in logs and core samples in Lance and Mesaverde Formations. Significant variation of porosity and permeability occurs within the sandstone zones. This variation warrants new rock typing approaches. We present rock typing for tight sandstones and siltstones with an understanding of petrophysical properties such as pore structure, porosity, permeability, and cementation.

We studied 94 samples from the Mesaverde and Lance Formations with lithologies varying from clean sandstone to shale. X-ray diffraction (XRD) mineralogy, mercury injection capillary pressure (MICP), helium porosity and permeability were measured for all samples. NMR transverse relaxation times (T2) at 2 MHz were also measured for 10 water saturated samples. Nitrogen adsorption was performed on 11 samples from Mesaverde formation to determine pore volume and pore size distribution.

MICP data are used to subdivide rocks into three groups based on pore throat size distribution: reservoir sandstones, non-reservoir sandstone and siltstone/mudstone. Dominant pore throat size for reservoir and non-reservoir sandstones are 400 and 100 nm, respectively. In order to apply pore throat size rock typing to downhole measurements, NMR pore size classification is used to identify formations. Pore size from NMR demonstrated equivalent behavior to pore throat size from MICP. The logarithmic mean values of T2 transverse relaxation times for reservoir, non-reservoir sandstone and siltstone/mudstone are 22.2 ms, 3.4 ms and 0.29 ms, respectively. Clear separation of reservoir sandstone, non-reservoir sandstone and siltstone is seen based on pressure dependency of ultrasonic compressional and shear velocities during initial pressure loading. Reservoir sandstone demonstrates the highest compressibility. In addition, siltstone and mudstone were separated based on log differential pore volume distribution from N2 adsorption data.

Based on pore size distribution data, four main rock types are identified in Lance and Mesaverde formations in Jonah field. Rock typing based on gamma ray and porosity logs can be considered as rock classification of end members. To capture transitional behavior in between end members, pore size distribution is needed in logging application. Since NMR T2 distribution show similar spectra to MICP throat size distribution, the rock typing technique can be applied using NMR log data. Separation of mudstone from siltstone can be used for identification of shale end points in log data.
Modeling Point Bars Using a Grid Transformation Scheme
Authors: Li Henry , Srinivasan Sanjay
Published: September 2015

Point bars, formed from meandering rivers, exhibit distinct heterogeneities. Modeling these heterogeneities is essential because of the presence of mud/silt layers that impede steam chamber rise in Steam-Assisted Gravity Drainage (SAGD). Previous point-bar models rely heavily on outcrops for geology. Such models may have difficulty extending to subsurface reservoirs where geological data is minimal. This paper details the modeling of point bars using geological trends and well data to supplement geostatistical simulation.

The modeling processes in this paper capture the internal geometry of the accretions layers as well as geological trends. Curvilinear grids are constructed between major erosional surfaces where a grid transformation scheme is used to transform the curvilinear coordinates into orthogonal coordinates for geostatistical simulation. The grid transformation allows flexibility of performing statistical simulation in rectangular coordinates while honoring the curvilinear geometry of the point bar. An entire point bar model contains a series of accreting curvilinear grids. Geological modeling is done independently for each grid. The geology captures key trends that make up the heterogeneities in the model.

The point bar model created is based on a modern point bar in the Brazos River. Data from thirty-three wells are used in creating the reservoir model. Several distinct trends are observed. There is an upward decrease in sediment size in the point bar. An overall fining downstream trend is observed in the vertical slices of the point bar. Heterolithic bedding is observed where frequent layers of silt extend from the top to near the base analogous to outcrops. Mud and silt dominate the upward regions of the point bar while conglomerate and cobble are mainly present at the base.

This paper presents a reservoir modeling process that properly define point bar geology using only well data and geological trends. Without reliance on outcrops, modeling of point bars can be applied to subsurface reservoirs. A detailed geologic model provides greater insight to the heterogeneities seen in point bar reservoirs. Well defined heterogeneities in point bar models is critical to any SAGD simulation.