bluuebunny/crossref_metadata_2025 huggingface dataset:
Rigorous Multi-Scenario Uncertainty Analysis: An Easy Way to Create an Ensemble of Many Concepts, with Hundreds of Uncertainties, and the Power of the Cloud to Evaluate Thousands of Realizations in Hours
Authors: Ashoori Elham , Van der Steen Erik H.
Published: September 2019

When key geological scenario uncertainties, captured in multiple conceptual models, are combined with continuous parameters, the evaluation of a representative sample set quickly becomes unmanageable, laborious and too time consuming to execute. A workflow is presented that enables users to easily model conceptual as well as parametric uncertainties of the reservoir without the necessity of any complex scripting. The chain of models for all concepts is presented in one view, to provide overview of the key differences between concepts used. An ensemble of geologically sound samples can be created taking into account parameter dependencies and probabilities of concepts. The chain of models per concept can easily be (re)executed.

A case study is presented that consists of multiple concepts based on different hierarchical stratigraphic models in combination with different fault models, each of which with its own fluid- (defined contacts per compartment), grid- (sub-layering and areal resolution) and rock property models. Volumetric calculations are run on an ensemble to get static model observables like GRV, Pore Volume, Oil-In-Place, etc., reported by multiple sub-regions of the model in combination with a lease boundary. (When coupled with dynamic simulation, observables like ultimate recovery, break-through timing, etc. could also be obtained). As thousands of realizations were run concurrently, run time was reduced from weeks to hours. Results reveal the distribution and dependency of observables like GRV on top-structure-depth uncertainty and contact-level uncertainty. For in-place volumes the full suite of concepts and other parametric uncertainties including the stochastic uncertainties (i.e. seed) is analyzed. This also enables the identification of the key uncertainties that impact equity the most, which can be of great commercial value during equity negotiations. This workflow demonstrates how, with the power of Cloud computing, rigorous evaluation of multiple concepts combined with many parametric uncertainties has been achieved within practical turn-around times. As such it overcomes the prohibitive hurdles of the past that often have led to simplifications necessary to save time and effort. The result is better decision quality in resource development decisions.
Production Optimisation Under Uncertainty with Automated Scenario Reduction: A Real-Field Case Application
Authors: Barros E. , Fonseca R. M. , de Moraes R. J.
Published: September 2019

In this work we discuss the successful application of our previously developed automated scenario reduction approach applied to life-cycle optimization of a real field case. The inherent uncertainty present in the description of reservoir properties motivates the use of an ensemble of model scenarios to achieve an optimized robust reservoir development strategy. In order to accurately span the range of uncertainties it is imperative to build a relatively large ensemble of model scenarios. The size of the ensemble is directly proportional to the computational effort required in robust optimization. For high-dimensional, complex field case models this implies that a large ensemble of model scenarios which albeit accurately captures the inherent uncertainties would be computationally infeasible to be utilized for robust optimization. One of the ways to circumvent this problem is to work with a reduced subset of model scenarios. Methods based on heuristics and ad-hoc rules exist to select this reduced subset. However, in most of the cases, the optimal number of model realizations must be known upfront. Excessively small number of realizations may result in a subset that does not always capture the span of uncertainties present, leading to sub-optimal optimization results. This raises the question on how to effectively select a subset that contains an optimal number of realizations which both is able to capture the uncertainties present and allow for a computationally efficient robust optimization. To answer this question we have developed an automated framework to select the reduced ensemble which has been applied to an original ensemble of 300 equiprobable model scenarios of a real field case. The methodology relies on the fact that, ideally, the distance between the cumulative distribution functions (CDF) of the objective function (OF) of the full and reduced ensembles should be minimal. This allows the method to determine the smallest subset of realizations that both spans the range of uncertainties and provides an OF CDF that is representative of the full ensemble based on a statistical metric. In this real field case application we optimize the injection rates throughout the assets life-cycle with expected cumulative oil production as the OF. The newly developed framework selected a small subset of 17 model scenarios out of the original ensemble which was used for robust optimization. The optimal injection strategy achieved an average increase of 6% in cumulative oil production with a significant reduction, approximately 90%, in the computational effort. Validation of this optimal strategy over the original ensemble lead to very similar improvements in cumulative oil production, highlighting the reliability and accuracy of our framework.
Optimization of Oil Field Development using a Surrogate Model: Case of Miscible Gas Injection
Authors: Simonov Maksim , Shubin Andrei , Penigin Artem , Perets Dmitrii , Belonogov Evgenii , Margarit Andrei
Published: September 2019

The topic of the paper is an approach to find optimal regimes of miscible gas injection into the reservoir to maximize cumulative oil production using a surrogate model. The sector simulation model of the real reservoir with a gas cap, which is in the first stage of development, was used as a basic model for surrogate model training. As the variable (control) parameters of the surrogate model parameters of gas injection into injection wells and the limitation of the gas factor of production wells were chosen. The target variable is the dynamics of oil production from the reservoir. A set of data has been created to train the surrogate model with various input parameters generated by the Latin hypercube.

Several machine learning models were tested on the data set: ARMA, SARIMAX and Random Forest. The Random Forest model showed the best match with simulation results. Based on this model, the task of gas injection optimization was solved in order to achieve maximum oil production for a given period. The optimization issue was solved by Monte Carlo method. The time to find the optimum based on the Random Forest model was 100 times shorter than it took to solve this problem using a simulator. The optimal solution was tested on a commercial simulator and it was found that the results between the surrogate model and the simulator differed by less than 9%.
Multivariate 3D Static and Dynamic Modeling Approach for an Oil Field at Early Stages of Exploration for a Fast-Track Project
Authors: Litvin Vitaly , Potapov Alexander , Snachev Mikhail
Published: September 2019

In this paper, the approach to multivariate static and dynamic modeling is considered on the example of an offshore field discovered in 2017. Based on the limited volume of information, the quantitative and qualitative description of uncertainties included further in the 3D modeling is made. This model is proposed to be used as a tool for prompt decision making when implementing a fast-track project with limited time between exploration and pre-FEED stages.
Optimization of Waterflooding Strategy Using Artificial Neural Networks
Authors: Bruyelle Jérémie , Guérillot Dominique
Published: September 2019

Waterflooding is the main technic to recover hydrocarbons in reservoirs. For a given set of wells (injectors and producers), the choice of injection/production parameters such as pressures, flow rates, and locations of these boundary conditions have a significant impact on the operating life of the wells. As a large number of combinations of these parameters are possible, one of the critical decision to make is to identify an optimal set of these parameters. Using the reservoir simulator directly to evaluate the impact of these sets being unrealistic considering the required number of simulations, a common approach consists of using response surfaces to approximate the reservoir simulator outputs. Several techniques involving proxies model (e.g., kriging, polynomial, and artificial neural network) have been suggested to replace the reservoir simulations. This paper focalizes on the application of artificial neural networks (ANN) as it is commonly admitted that the ANNs are the most efficient one due to their universal approximation capacity, i.e., capacity to reproduce any continuous function. This paper presents a complete workflow to optimize well parameters under waterflooding using an artificial neural network as a proxy model. The proposed methodology allows evaluating different production configurations that maximize the NPV according to a given risk. The optimized solutions can be analyzed with the efficient frontier plot and the Sharpe ratios. An application of the workflow to the Brugge field is presented in order to optimize the waterflooding strategy.
Assessment of H2S Production Risks in Heterogeneous Reservoirs Using Laboratory-Calibrated Compositional Thermal Reactive Simulations
Authors: Gasser-Dorado Julien , Ayache Simon Victor , Lamoureux-Var Violaine , Preux Christophe , Michel Pauline
Published: September 2019

SAGD is commonly used as a thermal EOR method to produce heavy oil. However it suffers from the production of acid gases formed by aquathermolysis chemical reactions that occur between the steam, the sulfur-rich oil and the mineral matrix. The objectives of this paper are to take advantage of a comprehensive chemical model coupled to compositional thermal reservoir simulations to predict and understand the H2S production variation at surface according to the type of reservoir.

Thermal reservoir simulations coupled to both a SARA based 10-component / 5-reaction chemical model fully calibrated against laboratory data and a compositional PVT are used to simulate SAGD processes on heavy oil fields in Athabasca, Canada. Numerical results are then analyzed to provide a comprehensive analysis of the mechanisms leading to in-situ H2S generation and its production at wellheads based on compositional thermal simulations coupled to a fully laboratory calibrated SARA-based chemical model. Composition of the pre-steam, post-steam and produced oil are compared to understand the effect of the aquathermolysis reactions. The impact of heterogeneities on H2S production both in-situ and at surface can also be observed and explained, especially the variations in vertical permeability. Then simple reservoir models with two facies are used to further understand the impact of heterogeneities on H2S production at surface. Overall heterogeneous cases show important changes in the temperature distribution, fluid flows, reactions kinetics and steam chamber shape that lead to H2S production variations at surface. This detailed description of the involved mechanisms in acid gases production will allow operators to better forecast their H2S risks according to their reservoir properties.
3D Model Validation in Reservoir Management Optimization Workflow
Authors: Luciani Italo , Renna Simona , Ortega Angelo , Catalani Carlo , Mastrogeorgiou Konstantinos , Cortese Andrea
Published: September 2019

3D model is a valuable tool in reservoir management, provided its representativeness of reservoir dynamics.Traditional History Match mainly focuses on reproducing reservoir behavior at well scale. A good match is not always representative of fluid movements in the reservoir. The proposed approach for 3D model validation combines and compares the results of integrated production analysis, in particular flow paths identification, with history matching by using streamlines technology. Streamlines speed up the comparison process especially in complex 3D models.

The workflow is based on a massive Production Data Analysis (PDA) where geological and dynamic data are integrated to identify preferential paths followed by the different fluid phases during the producing life of the field. The main result is the Fluid Path Conceptual Model (FPCM) where aquifer and injected water movements are clearly identified. Once the flooded areas are detected, streamlines are traced on the history matched model in order to easily compare the simulated connections with hard information from PDA. Actions to improve the model representativeness are suggested and integrated in an iterative tuning process.

This paper presents the results of the methodology applied on two complex fields with different injection strategies. FPCMs resulting from PDA provided a powerful boost to drive the history match and speed up the whole process. Priority was given in reproducing the identified preferential paths rather than to perfectly match well production data (which can be also affected by allocation uncertainties) by means of local unrealistic adjustments.

Streamlines were run on Intersect simulation, proving to be a fast and powerful tool for the visualization and understanding of fluid movements in the 3D Model. Since streamlines are used as visualization tool and are traced on a corner point geometry grid using fluxes provided by reservoir simulation, the reliability of the simulation output is preserved.

Once the model is representative of the real field behavior, it can be used as predictive tool in Reservoir Management to optimize the current injection strategy, promoting most efficient injectors.
Leveraging Data Analytics with Numerical Modeling for Optimizing Oil Field Development and Management
Authors: Alklih Mohamad Yousef , Tengku Ab Hamid Tengku Mohd , Alshabibi Tariq Ali , Mohagheg Shahab
Published: September 2019

Data-Driven subsurface modeling technology has been proven, for the past few years, to yield technical and commercial success in several oil fields worldwide. A data-driven model is constructed for the first time for an oil field onshore Abu Dhabi, and used for evaluation of a reservoir with substantial reserves and comprehensive development plan; for the purpose of predicting production rates, dynamic reservoir pressure and water saturation, improving reservoir understanding, supporting field development optimization and identifying optimum infill well locations. The objective is to provide the asset with a decision-support tool to make better field development planning and management.

The subject reservoir is a low permeability carbonate reservoir and characterized by lateral and vertical variations in its reservoir rocks and fluid properties. More than 8 years of Phase-I development and production/injection data and extensive amount of well tests and log data (SCAL, PVT, MDT) from more than 37 wells were used to construct the Data Driven Model for this asset.

This new modeling technology, (TDM), integrates reservoir engineering analytical techniques with Artificial Intelligence, Machine Learning & Data Mining in order to formulate an empirical and spatiotemporally calibrated full field model. In this work, it is leveraged with other conventional reservoir modeling and management tools such as streamline modeling, isobaric maps and flooding conformance.

Several analyses were performed using the full field data-driven model; complementing the existing conventional numerical model. The accomplishments of the data-driven reservoir model for this project included, but not limited to, comprehensive history matching (including blind validation) and then forecast of Oil rate, GOR, WC, reservoir pressure and water saturation, injection optimization, and choke size optimization. The results generated by the data-driven model proved to be quite eye-opening for the asset management; as the model was able to identify potential areas of improving field efficiency and cost reduction.

When combined with numerical techniques, the calibrated data-driven model assist to obtain a reliable short term forecast in a shorter time and help make quick decisions on day-to-day operational optimization aspects. The use of facts (all field measurements) instead of human biases, pre-conceived notions, and gross approximations distinguishes data-driven modeling from other existing modeling technologies. Its innovative combination of Artificial Intelligence and Machine Learning (the technologies that are transforming all industries in the 21st century) with reservoir engineering, reservoir modeling and reservoir management clearly demonstrates the potentials that these pattern recognition technologies offer to the upstream oil and gas industry for its realistic digital transformation.
Seismic Characterisation using Artificial Intelligence Algorithms for Rock Property Distribution in Static Modeling
Authors: Mata García Sergio Roberto , Carrasco Hernández Javier , Ortiz López José L.
Published: September 2019

This study examines which is the margin of usability for Artificial Intelligence (AI) algorithms related to the rock properties distribution in static modeling. This novel method shows a forward modeling approach using neural networks and genetic algorithms to optimize correlation patterns among seismic traces of stack volumes and well rock properties. Once a set of nonlinear functions is optimized in the well locations, to correlate seismic traces and rock properties, spatial response is estimated using the seismic volume. This seismic characterization process is directly dependent on the error minimization during the structural seismic interpretation process, as well as, honoring the structural complexity while modeling. Previous points are key elements to obtain an adequate correlation between well data and seismic traces. The joint mechanism of neural networks and genetic algorithms globally optimize the nonlinear functions and its parameters to minimize the cost function. Estimated objective function correlates well rock properties with seismic stack data. This mechanism is applied to real data, within a high structural complexity and several wells. As an output, calibrated petrophysical time volumes in the interval of interest are obtained. Properties are used initially to generate a geological facies model. Subsequently, facies and seismic properties are used for the three-dimensional distribution of petrophysical properties such as: rock type, porosity, clay volume and permeability. Therefore, artificial intelligence algorithms can be widely exploited for uncertainty reduction within the rock property spatial estimation.
A Method to Quantitatively Characterize Tight Glutenite Reservoir Pore Structure
Authors: Dong Xuemei , Zhang Ting , Yao Weijiang , Hu Tingting , Li Jing , Jia Chunming , Guan Jian
Published: September 2019

Pore structure is of great importance in tight reservoirs identification and validation evaluation, especially for formations with developed fractured. However, the conventional pore structure evaluation method based on nuclear magnetic resonance (NMR) logging lost its role. This is because the fractures with width lower than 2mm did not have response in the NMR T2 spectrum. Whereas the porosity spectrum, which extracted from the FMI data, was considered to be effective in fractured reservoir pore structure evaluation. In this study, to quantitatively characterize tight glutenite reservoir pore structure in the Jiamuhe Formation in northwest margin of Junggar Basin, northwest China, 90 core samples were drilled for lab mercury injection capillary pressure (MICP) measurement, and the XRMI data (acquired by the Halliburton and be similar with FMI) was processed to acquire the porosity spectrum. The relationship between the MICP curve and the corresponding inverse cumulative curve of porosity spectra was analyzed, and the model of piecewise power function, which can be used to transform the porosity spectrum as pseudo capillary pressure (Pc) curve, was established. By using this model, consecutive pseudoPc curves can be constructed in the intervals with which XRMI data was acquired, and the corresponding pore structure evaluation parameters, such as the average pore throat radius, the maximum pore throat radius, the threshold pressure, and so on, can also be predicted. Meanwhile, a permeability prediction model based on the Swanson parameter, also established. By combining with the constructed consecutive pseudoPc curves, the pore structure evaluation parameters and permeabilities, several hydrocarbon production potential formations were identified, and this was verified by the drill stem test (DST) data.
Smart Processing and Analysis of Image Log Data: A Digital Approach for a Robust Facies Modelling in Heterogeneous Carbonate Reservoirs
Authors: Galli Maria-Teresa , Berto Roberto , Buongiovanni Giuliana , Pirrone Marco
Published: September 2019

This paper discusses the use of a novel data-driven method for automated facies classification and characterization of carbonate reservoirs. The approach makes an extensive use of wireline and while drilling electrical borehole image logs and provides a direct and fast recognition of the main geological features at multi-scale level, together with secondary porosity estimation. This embodies an unbiased and valuable key-driver for rock typing, dynamic behavior understanding and reservoir modeling purposes in these puzzling scenarios.

The implemented methodology takes advantage of a non-conventional approach to the analysis and interpretation of image logs, based upon image processing and automatic classification techniques applied in a structural and petrophysical framework. In particular, the Multi-Resolution Graph-based Clustering (MRGC) algorithm that is able to automatically shed light on the significant patterns hidden in a given image log dataset. This allows the system to perform an objective multi-well analysis within a time-efficient template. A further characterization of the facies can be established by means of the Watershed Transform (WT) approach, based on digital image segmentation processes and which is mainly aimed at quantitative porosity partition (primary and secondary).

The added value from this data-driven image log analysis is demonstrated through selected case studies coming from vertical and sub-horizontal wells in carbonate reservoirs characterized by high heterogeneity. First, the MRGC has been carried out in order to obtain an alternative log-facies classification with an inherent textural meaning. Next, the WT-based algorithm provided a robust quantification of the secondary porosity contribution to total porosity, in terms of connected vugs, isolated vugs, fractures and matrix contribution rates. Finally, image log-facies classification and quantitative porosity partition have been integrated with production logs and pressure transient analyses to reconcile the obtained carbonate rock types with the effective fluid flows and the associated dynamic behavior at well scale.

The presented novel methodology is deemed able to perform an automatic, objective and advanced interpretation of field-scale image log datasets, avoiding time-consuming conventional processes and inefficient standard analyses when the number of wells to be handled is large and/or in harsh circumstances. Moreover, secondary porosity can be proficiently identified, evaluated and also characterized from the dynamic standpoint, hence representing a valuable information for any 3D reservoir models.
Distribution Mechanism of Asphaltene Deposits in CO2 Flooding Path: Interpretation by Numerical Model Based on Experimental Observation
Authors: Yonebayashi Hideharu , Iwama Hiroki , Takabayashi Katsumo , Miyagawa Yoshihiro , Watanabe Takumi
Published: September 2019

CO2 injection is one of widely applied enhanced oil recovery (EOR) techniques, moreover, it is expected to contribute to the climate change from a viewpoint of storing CO2 in reservoir. However, CO2 is well known to accelerate precipitating asphaltenes which often deteriorate production. To understand in-situ asphaltene-depositions, unevenly distributed in composite carbonate core during a CO2 flood test under reservoir conditions, were investigated through numerical modelling study.

Tertiary mode CO2 core flood tests were performed. A core holder was vertically placed in an oven to maintain reservoir temperature and to avoid vertical segregation. A composite core consisting of four Ø1.5" × L2.75" plug cores, which had similar porosity range but slightly varied air permeabilities, was retrieved from a core holder after the flooding test. The remaining hydrocarbon was extracted by Dean-stark method, and heptane insoluble materials were extracted from each plug core via IP-143 method to observe distribution of asphaltene deposits. The variation of asphaltene mass in plug cores was investigated to explain its mechanism thermodynamically.

The core flood test was completed to achieve a certain additional oil recovery by 15 pore volume CO2 injection without any unfavorable differential pressure. The remaining asphaltene mass in each plug core revealed a trend in which more asphaltene collected from the inlet-side core. We assumed a scenario to explain the uneven asphaltene distribution by incorporating the vaporized-gas-drive and CO2 condensing mechanism. Namely, asphaltenes deposited immediately when pure CO2 contacted with oil. The contact between more pure CO2 and oil might be more frequently occurred in inlet-side core. To reproduce the scenario, a cubic-plus-association (CPA) model was generated to estimate asphaltene precipitating behavior as injected gas composition varied. In the first plug core, more pure CO2 gas was considered to contact with fresh reservoir oil compared with the downstream cores which might have less pure CO2 because of its condensation. The light-intermediate hydrocarbon gas vaporized by CO2 was also considered to emphasize the trend of more asphaltene deposits in upstream-side cores. The CPA model revealed consistent phenomenon supporting the scenario.
A Pragmatic Approach to Reservoir Simulation Optimisation Under Uncertainty
Authors: Kathrada Muhammad , Azri Khairul
Published: September 2019

Reservoir simulation optimization under uncertainty typically invokes a sense of anxiety mainly because of a lack of a systematic criterion to choose between different development scenarios under uncertainty, how to go about doing well placement and optimizing well controls in the face of a large uncertainty ensemble of static realisations, and most of all the large number of simulation runs that potentially needs to be conducted. This is exacerbated when the models are large and require many hours to run. Moreover, even with the prevalence of distributed and parallel computing clusters, there is still a limited amount of computing resources available when spread out over the number of reservoir engineers within a company. Time and budget constraints also contribute to complicating this process. Furthermore, with the requirement of an inordinately large number of simulation runs comes the dilemma as to which optimizer to choose that would help speed up the process.

This paper first starts off with a brief background into historical attempts at tackling this problem by delving into the literature. Then it discusses a rigorous criterion for optimization under uncertainty viz. stochastic dominance, hitherto little known or used in the industry. A commonly used greenfield case study which is an ensemble set of uncertainty realisations is then introduced, which the rest of the paper will be based on. The ensemble is a pre-generated set of fifty realisations designed specifically for this problem. Two challenging areas will then be addressed viz. well placement optmisation under uncertainty, and well controls optimization under uncertainty.

Finally, a comparison between the simplex, proxy response surface, differential evolution and particle swarm optimization methods is made in the optimization of well controls. Hence the paper aims to give a complete picture on how to go about reservoir simulation optimization under uncertainty, with a drastically reduced amount of computational runs that needs to be conducted. Practical and sensible formulation of the optimization problemcan go a long way to making this process more understandable and easier to implement.
Geology-Based Porosity-Permeability Correlations in Carbonate Rock Types
Authors: Dernaika Moustafa , Masalmeh Shehadeh , Mansour Bashar , Al Jallad Osama , Koronfol Safouh
Published: September 2019

In carbonate reservoirs, permeability prediction is often difficult due to the influence of various geological variables that control fluid flow. Many attempts have been made to calculate permeability from porosity by using theoretical and empirical equations. The suggested permeability models have been questionable in carbonates due to inherent heterogeneity and complex pore systems. The main objective of this paper is to resolve the porosity-permeability relationships and evaluate existing models for predicting permeability in different carbonate rock types.

Over 1000 core plugs were studied from 7 different carbonate reservoirs across the Middle East region; mainly cretaceous reservoirs. The plugs were carefully selected to represent main property variations in the cored intervals. The data set available included laboratory-measured helium porosity, gas permeability, thin-section photomicrographs and high-pressure mercury injection. Plug-scale X-ray CT imaging was acquired to ensure the samples were free of induced fractures and other anomalies that can affect the permeability measurement. Rock textures were analyzed in the thin-section photomicrographs and were classified based on their content as grainy, muddy and mixed. Special attention was given to the diagenesis effects mainly compaction, cementation and dissolution.

The texture information was plotted in the porosity-permeability domain, and was found to produce three distinct porosity-permeability relationships. Each texture gave unique poro-perm trend, where the extent of the trend was controlled by diagenesis. Rock types were defined on each trend by detailed texture analysis and capillary pressure. Three different permeability equations (Kozney, Winland, Lucia) were evaluated to study their effectiveness in complex carbonate reservoirs. A new permeability equation was proposed to enhance the prediction results of the experimental data.

Rock types were successfully classified based on porosity, permeability, capillarity and textural facies. Conclusive porosity-permeability relationships were obtained from textural rock properties and diagenesis, which were linked to rock types using capillary pressure. The texture-diagenesis based rock types provided more insight into the effects of geology on fluid flow and saturation. Available models may not fully describe permeability in heterogeneous rocks but they can improve our understanding of fluid flow characteristics and predict permeability in un-cored wells.
An Adjoint Inexact Trust Region Method for Nonlinear Constraint Production Optimization
Authors: Chakra Chithra , Al Kobaisi Mohammed
Published: September 2019

Production optimization is the method for seeking the best possible well control and schedule plans in order to enhance reservoir performance under a given state and economic constraints. Determining the optimal injection and production control strategies through adjoint gradient-based optimization is a well-known practice in today’s modern reservoir management. However, apt handling of nonlinear control inputs, state and output constraints can be quite tedious with effects on the computational efficiency of the optimization algorithms used in practical production optimal control problems. In this paper, we develop an adjoint based interior-point inexact trust filter sequential quadratic programming (IITRF-SQP) method for solving constrained production optimization problems. Inexact trust-region is an extension of a filter trust region approach, which is used when the control input constraints Jacobians are of high dimension and are expensive to compute. The output constraints are handled using an interior-point method called- modified barrier-augmented Lagrangian, in which inequality constraints are treated by a modified barrier term and equality constraints with augmented Lagrangian terms. The algorithm we present uses the approximate information of Jacobians achieved through composite-step computation, which eliminates the cost of direct calculation of Jacobians and Hessians (gradients). The gradient information that provides criticality measure of the objective function is calculated using the adjoint method. Two numerical experiments on optimal water-flooding are presented. Performance comparisons of the proposed IITRF-SQP method with Lagrangian barrier method and sequential linear quadratic programming (SLQP) for solving production optimization problem are carried out. Results indicate that the gradient-based adjoint coupled with IITRF-SQP was able to improve net present value (NPV) through optimal production profiles with better computational efficacy via reduced convergence time and number of gradient and objective function evaluations.
Closed-Loop Integrated Time-Lapse Seismic Feasibility in Amberjack Field – Deepwater Offshore Gulf of Mexico
Authors: Thibodeaux Brandon , Ramsay Travis , Segovia Felix , Hernandez Luisalic , Ibrahim Mohammed
Published: September 2019

A flow simulation-driven time-lapse seismic feasibility study is performed for the Amberjack field that leverages existing multi-vintage 4D time-lapse seismic data. The focus is a field consisting of stacked shelf and deepwater reservoir sands situated in the Gulf of Mexico in Mississippi Canyon Block 109 in 1,030 ft of water. The solution leverages seismic interpretation, seismic inversion, earth modeling, and reservoir simulation [including embedded petro-elastic modeling (PEM) capabilities] to enable the reconciliation of data across multiple seismic vintages and forecast the optimal future seismic survey acquisition in a closed-loop. The overarching feasibility solution is integrated and simulation-driven involving multi-vintage seismic inversion, spatially constraining the petrophysical property model by seismic inversion, and performing reservoir simulation with the embedded PEM. The PEM is used to compute P-impedance and Vp/Vs dynamically, which enables tuning to both historical production and multi-vintage seismic data. The process considers a hybrid fine-scale 3D geocellular model in which the only upscaling of petrophysical properties occurs when the P-impedance from seismic inversion is blocked to the 3D geocellular grid. This process minimizes resampling errors and promotes direct tuning of the simulator response with registered seismic that has been blocked to a geocellular earth model grid. The results illustrate a three-part simulation-to-seismic calibration procedure that culminates with a prediction step which leads to a simulation-proposed time-lapse seismic acquisition timeline that is consistent with the calibrated reservoir simulation model. The first calibration tunes the model to historical production profiles. The second calibration reconciles the dynamic P-impedance estimate of the simulated shallow reservoir with that of the seismic inversion blocked to the 3D geocellular grid. The combination of these two steps outline a seismic-driven history matching process whereby the simulation model is not only consistent with production data but also the subsurface geologic and fluid saturation description. Large and short wavelength disparities in the P-impedance calibration existing between the simulator response and the time-lapse seismic data are attributed to resampling errors as a result of seismic inversion-derived P-impedance being blocked to the 3D geocelluar grid, as well as sparse well control in the earth model which leads to the obscuring of some asset-specific characteristics. The results of the third calibration step show how the time-lapse seismic feasibility solution accurately confirms prior seismic surveys undertaken in the asset. Given this confirmation, the solution achieves a suitable prediction of seismic-derived rock property response from the reservoir simulator as well as the optimal future time-lapse seismic acquisition time.
Assessment of Fishbone Well Design Performance in a Tight Carbonate Compared to Single Extra-Long MRC Lateral
Authors: Alyan Mohand , Al Sowaidi Alunood
Published: September 2019

A reservoir simulation study of different fishbone well designs performance compared to a base development well design of extra-long maximum reservoir contact (MRC) single lateral wells is presented. The objective is to compare different well design concepts in a waterflood recovery scheme to achieve production target rate and maximize resource value for economic development of an undeveloped tight carbonate reservoir. The studied reservoir is located in a giant offshore oil field in the Middle East and was used as a representation of the different tight reservoirs within the field. It is characterized by poor quality rocks with a permeability trending from 2 – 0.5 md in a SE – NW direction.

The study compromises an assessment of the achievable initial maximum oil rate, volumetric reservoir sweep and expected ultimate oil recovery factor for different well design concepts for a base short well spacing utilized for effective pressure support. In addition to that, the impact of fishbone well design on well count reduction potential utilizing twice the base short well spacing compared to single lateral wells development design utilizing the base short well spacing was evaluated.

A sector model with equal producer to injector ratio was used with refined gridding to wells and bulk area gridded with a cell size of 10 m by 10 m in a representative area of the reservoir. The modeled wells incorporated with vertical flow performance tables with gas lift capabilities. The analysis also incorporated generating streamlines for analyzing fishbone well designs areal reservoir sweep and an examination of remaining movable oil areal distribution. An assessment matrix was formulated for comparing extra-long MRC single laterals base development design versus different fishbone well designs. The assessment matrix incorporated in addition to reservoir related flow performance indicators: drilling complexity and well cost, well life cycle activities, etc. for a comprehensive assessment.

The main findings show that fishbone well designs have complicated areal sweep performance, especially with sealed motherbore, that result in a lower oil recovery factor with higher hydrocarbon pore volume injected and water oil ratio compared to extra-long MRC single laterals. Also, fishbone well designs have serious limitations during well life cycle activities compared to extra-long MRC single lateral design in terms of stimulation, well accessibility and well intervention options making the extra-long MRC single laterals the preferred field development concept within tight reservoirs especially with the base short well spacing. Finally, the analysis has shown that Fishbone well designs can’t reduce the well count since base short well spacing is still needed for effective pressure support by water injection in addition to maximizing the oil recovery factor within the field life time and building and sustaining the target plateau.
Visual Recognition of Drill Cuttings Lithologies Using Convolutional Neural Networks to Aid Reservoir Characterisation
Authors: Kathrada Muhammad , Adillah Benjamin Jacob
Published: September 2019

There are a vast number of reservoirs with drill cuttings and core images that have classification problems associated with them. This could be due to the images not being classified in the first place, or the images may be available but the interpretation reports could be missing. Another problem is that images from different wells could be interpreted by different wellsite geologists/sedimentologists and hence result in an inconsistent classification scheme. Finally, there could also be the problem of some images being incorrectly classified. Ergo it would be desirable to have an unbiased objective system that could overcome all of these issues. Step in convolutional neural networks. Advances during this decade in using convolutional neural networks for visual recognition of discriminately different objects means that now object recognition can be achieved to a significant extent. Once the network is trained on a representative set of lithological classes, then such a system just needs to be fed the raw drill cuttings or core images that it has not seen before and it will automatically assign a lithological class to each image and an associated probability of the image belonging to that class. In so doing, images below a certain probability threshold can be automatically flagged for further human investigation. The benefit of such a system would be to improve reservoir understanding by having all available images classified in a consistent manner hence keeping the characterization consistent as well. It would further help to reduce the time taken to get human expertise to complete the task, as well as the associated cost.
Improvement of the SAGD Process by Use of Steam-Foam: Design and Assessment of a Pilot Through Reservoir Simulation
Authors: Abdul Ghani Mohamad , Ayache Simon Victor , Batôt Guillaume , Gasser-Dorado Julien , Delamaide Eric
Published: September 2019

Although SAGD is a very popular in-situ extraction method in Canada, this thermal process relies on huge energy and water consumption to generate the steam. Irregular growth of the steam-chamber due to heterogeneities further degrades its yield. Contact between the steam chamber and the overburden also leads to heat losses. The objective of this paper is to investigate how Foam Assisted-SAGD could mitigate these technical issues and improve the efficiency of the SAGD process. Compositional thermal reservoir simulations are used to simulate and analyze a Foam Assisted-SAGD pilot. The shear-thinning effect close to the wells is also accounted for. The simulations are run on a homogeneous model mimicking the Foster Creek project in Alberta, Canada. Several type of injection sequences have been analyzed in terms of foam formation, back-produced surfactants and cumulative Steam-Oil-Ratio. Results are compared with the original SAGD performance. In order to propagate the foaming surfactants throughout the steam chamber the injection sequence needs to be properly determined. A simple continuous Foam Assisted-SAGD injection would lead to an accumulation of surfactant between the wells due to gravity segregation, preventing the foam from acting on the upper part of the steam chamber. Furthermore surfactant production occurs after a few weeks due to the proximity of the producer and the injector. A proper injection strategy of the type SAGD/slug/SAGD/slug is found to delay the chemical breakthrough and increase the amount of surfactant retained in the reservoir while allowing the surfactant propagation throughout the steam chamber. After optimization the Foam Assisted-SAGD process appears to be technically promising.
Oilfield Performance in the Presence of Asphaltene Using a Dynamic Flow Simulation
Authors: Al-Qasim Abdulaziz , AlAnazi Amer , Miftakhov Ruslan
Published: September 2019

This paper discusses a method for optimizing production and operation for onshore/offshore wells. Optimizing the production of oil and gas fields necessitates the use of accurate predication techniques to minimize uncertainties associated with day-to-day operational challenges related to serious operational problems caused by asphaltene deposition. It involves the use of a dynamic flow simulator for modeling oil and gas production systems and reservoir management to determine the feasibility of its economic development. Many studies have focused on relating asphaltene precipitation flocculation and deposition in oil reservoirs and flow assurance in the wellbores. Experimental techniques and theoretical models have been developed trying to understand and predict asphaltene behavior. Nevertheless, some ambiguities still remain with regard to the characterization asphaltene in crude oil and its stability during the primary, secondary, and tertiary recovery stages within the near-wellbore regions.

A synthetic onshore full-field scale that is based on a heterogeneous three-dimensional Cartesian single-well model is considered in this paper. Two wells (a producer and an injector) and one reservoirs are considered to evaluate the dynamic properties under the influence of asphaltene. The size of the reservoir is 25 ft × 25ft × 20 ft and is represented by grid numbers of 50 columns × 50 rows × 5 layers with 12 hydrocarbon components constituting the constant crude composition of this model. The model comprised a total of 12,500 grid blocks. The three-dimensional simulation employed 5-layers, incorporating all relevant production and reservoir data. Different production scenarios were investigated to define the most appropriate and efficient production strategy. This paper provides a method to assess the effect of asphaltene precipitation, flocculation, and deposition in the well productivity and the economic impacts related to it and investigating prevention techniques and other related in-situ pore level flow assurance parameters.

The results will include a comparison of production rates with and without asphaltene precipitation, flocculation, and deposition. In addition, it provides a comparison of asphaltene precipitation, flocculation, and deposition at different times using varying bottomhole and production rate constraints. Several cases (i.e., WAG cycles, completion, target layers of injection, etc.) are tested to help in selection of the optimum completion and operating strategy in the presences asphaltene. The paper will provide insight into factors affecting the flow assurance of oil and gas reservoirs.