Integrating Flow Simulation With Multi-Component Time-Lapse Seismic: Application to Postle Field CO2 EOR Project

Rock typing is an essential part of building geologic model for an asset. Millions of dollars are invested in logs, core measurements, SCAL studies, and geological interpretation that result in definition of different rock types. Rock types overlap and do not have crisp boundaries.;Upon definition of rock types for a series of geological formations geoscientists use approximation of multiple and overlapping rock types to identify a dominant rock type for any grid block in a reservoir simulation model. This defeats the original purpose of performing detail geological, petrophysical studies as far as reservoir flow models are concerned.;The objective of this study is to develop a new and novel methodology based on performing "fuzzy rock typing". Fuzzy rock typing refers to application of fuzzy set theory to the part of reservoir characterization that is concerned with rock type determination. Fuzzy set theory is applied in order to take into account the inherent uncertainties and vagueness associated with rock typing in hydrocarbon bearing reservoirs.;In this work, a numerical simulator has been used as the control environment in order to set up multiple studies that would demonstrate the differences between using conventional (current practices) approach of implementation of geologic models in the reservoir flow simulation studies and the new approach that is the subject of this study. By using the numerical reservoir simulator as the control environment it is intended to study the complexities that exist in upscaling the high resolution geological model using two different approaches.;The high resolution geological model used in its entirety and the flow simulation is performed. The results (production profiles) are compared to first, the upscaled model using conventional (current) practices and then the upscaled model using the proposed technique. The results are analyzed in order to demonstrate the difference between the two techniques and the advantages and disadvantages of each have been identified.

We describe a novel approach for up-scaling of geological models of heterogeneous reservoirs. The method, which is based on the use of wavelet transformations, incorporates all the relevant data on the permeability, porosity, and other important properties of a reservoir at all the important scales. It generates a nonuniform computational grid which preserves its resolved structure in the regions around the wells and in the high-permeability zones, but is scaled up in the rest of the geological model. To demonstrate the accuracy and efficiency of the method, we apply it to the study of three important problems, namely, the pressure transient analysis, unstable miscible displacements, and two-phase flows. In the study of multiphase flows, we apply a new and robust front detection method. The numerical dispersion is eliminated by a high-order total variation diminishing (TVD) method which yields results that are as accurate as those obtained by the dual-mesh methods, but with a greatly-reduced computational cost. The speed-up in the computations is up to three orders of magnitude. 1. Introduction Computer simulation of unstable miscible displacements and multiphase flows in petroleum reservoirs involves intensive computations1. Advances in measurement and estimation techniques, together with considerable progress in the development of methods for characterization of petroleum reservoirs, can provide significant amounts of data on various properties of the reservoirs. Using such data, modern geostatistical techniques generate highly resolved geological models of heterogeneous petroleum reservoirs. To carry out reservoir simulations, the governing flow and transport equations are discretized, and the flow domain is divided into grid blocks which are characterized by the associated rock properties. Due to computational limitations (mainly computation time) it is not feasible to carry out multiphase flow simulations with a computational grid that corresponds to the highly resolved geological model. It is, therefore, necessary to up-scale the properties of the grid blocks of the geological model in order to develop a coarsened grid that can be used in reservoir simulation with a reasonable amount of computation time. Up-scaling also plays a critical role in reservoir planning and management. In the past, it was common to forecast production based on reservoir simulations that used deterministic models of reservoirs - those that did not take into account the effect of the uncertainties in the distributions of the reservoirs' properties. However, deterministic models yield predictions that may be more harmful to reservoir planning and management than not having a model at all, since the appraisal of the potential production of a petroleum field must take into account the effect of the uncertainties in the properties of the field. Under such conditions, production forecast obtained by a stochastic model of a reservoir allows the quantification of the effect of the uncertainties on the reservoir performance by simulation of fluid flow in many possible realizations of the same reservoir, which are necessarily very time consuming. To reduce the simulation time, up scaling of the geological model is a natural and, perhaps, the only feasible way. For simulation of single-phase flows, the most important parameter is the absolute permeability, for which up-scaling methods are well-established. However, simulation of multiphase flows involves, in addition to computing the equivalent absolute permeabilities of the coarsened blocks, adjustments to the phase flow through the connected blocks of the coarsened grid. To accomplish this many methods have been proposed, including up-scaling techniques that employ pseudo-functions, the so-called dual-mesh approaches, and simpler non-uniform coarsening methods.

Using commercial numerical reservoir simulators to build a full field reservoir model and simultaneously history match multiple dynamic variables for a highly complex, offshore mature field in Malaysia, had proven to be challenging, manpower intensive, highly expensive, and not very successful. This field includes almost two hundred wells that have been completed in more than 60 different, non-continuous reservoir layers. The field has been producing oil, gas and water for decades. The objective of this article is to demonstrate how Artificial Intelligence (AI) and Machine Learning is used to build a purely data-driven reservoir simulation model that successfully history match all the dynamic variables for all the wells in this field and subsequently used for production forecast. The model has been validated in space and time. The AI and Machine Learning technology that was used to build the dynamic reservoir simulation and modeling is called spatio-temporal learning. Spatio-temporal learning is a machine-learning algorithm specifically developed for data-driven modeling of the physics of fluid flow through porous media. Spatio-temporal learning is used in the context of Deconvolutional Neural Networks. In this article Spatio-temporal Learning and Deconvolutional Neural Networks will be explained. This new technology is the result of more than 20 years of research and development in the application of AI and Machine Learning in reservoir modeling. This technology develops a coupled reservoir and wellbore model that for this particular oil & gas field in Malaysia uses choke setting, well-head pressure and well-head temperature as input and simultaneously history matches Oil production, GOR, WC, reservoir pressure, and water saturation for more than a hundred wells through a completely automated process. Once the data-driven reservoir model is developed and history matched, it is blind validated in space and time in order to establish a reliable and robust reservoir model to be used for decision making purposes and opportunity generation to maximise the field value. The concepts and the methodology of history match of multiple wells, individual offshore platforms, and the entire field will be presented in this article along with the results of blind validation and production forecasting. Results of using this model to perform uncertainty quantification will also be presented. A case study of a highly complex mature field with large number of wells and years of production has been used to be studied and simulated by this data-driven approach. Time, efforts, and resources required for the development of the dynamic reservoir simulation models using AI and Machine Learning is considerably less than time and resources required using the commercial numerical simulators. It is validated that the TDM developed model can make very reasonable prediction of field behavior and production from the existing wells based on modification of operational constraints and can be a prudent complementary tool to conventional numerical simulators for such complex assets.

Simulation of multiphase flow systems are of critical importance in managing hydrological systems. Flow simulations are affected by a number of factors including structure and flow properties including porosity and permeability as well as the anisotropy and heterogeneity of these properties. In many cases traditional hydrological and reservoir data are highly affected by these parameters, but are not directly sensitive to them. As such modellers often adjust these parameters in an ad hoc manner until solutions numerically converge. Simulation models are generally based on structural data from reflection seismics whose physical flow properties are then populated using geostatistical extrapolation techniques utilizing a sparse number of borehole logs and core analysis. In multiphase systems including enhanced oil recovery and carbon capture and sequestration uncertainties regarding phase-dependent physical properties confounds this challenge further. Geophysical methods provide a means by which to gain an improved understanding of phase distributions in the subsurface. In this paper we will look at applications from active carbon capture and sequestration and enhanced oil recovery applications, as well as synthetic examples. Geophysical data including electromagnetic and gravity are inverted using structural constraints from the reservoir model. Inversions are then mapped into flow properties using calibrated relations such as Archie’s Equation. The coupled models can then be used to both verify and improve on the reservoir flow model which improves it’s predictive power and utility as a management tool.

Effective reservoir characterization approaches aim to integrate all available and reliable sources of data to mitigate the effects of uncertainty of reservoir parameters and have a robust reservoir model(s). Reservoir permeability is one of the most critical, and usually, uncertain parameters in reservoir simulation models. The permeability model has a direct impact on production forecasts and consequently the economics of decisions made based on reservoir models. Having a calibrated permeability model is a challenging task. In most cases, the permeability model undergoes several modifications to achieve a history-matched reservoir model that honors the historical pressure and production data. The objective of this work is to show a workflow that integrates two different types of data sources to help calibrate the permeability of reservoir models. The proposed approach relies on adjusting the permeability of reservoir models by integrating seismic amplitude values and data from wells’ pressure transient analyses. The seismic amplitude data in the vicinity of a candidate well is correlated with permeability obtained from recorded pressure transient data in a systematic way. The generated correlation is then applied to the entire reservoir model in order to calibrate the permeability value of each grid block in the model. The application of the proposed approach on actual field data is also presented. As a result, the seismic and pressure transient data both can make valuable contributions towards calibrating geo-cellular 3D reservoir models at their respective and different scales.

S.B. Rachmawati, Rick J. Sustakoski, Timothy P. Whitacre, P.T. Caltex Pacific Indonesia, David J. Goggin, Marjorie Levy, Alan B. Bernath, Kemal Anbarci, Chevron Petroleum Technology Company, Gary Wu, Texaco Exploration and Production Company Abstract Reservoir characterization involves the quantification, integration, reduction, and analysis of geological. petrophysical, seismic, and engineering data. The principal goal of reservoir characterization is to derive a spatial understanding of interval heterogeneity. The result of this analysis can be improved dramatically using 3-D interpretation and analysis techniques. The product of 3D reservoir characterization is a 3D reservoir model which is largely a function of the stratigraphic framework. Sequence stratigraphy concepts are used to identify and analyze five major depositional sequences in the early Miocene to middle Miocene Sihapas Group in the Northwest Segment of the Minas Field. The purpose of this paper is to discuss the process and results of a combined, 3-D deterministic and stochastic reservoir modeling procedure. Three-dimensional reservoir modeling results in an improved geologic interpretation while providing an integrated approach and allowing for immediate model updates as a new data is acquired. The geologic modeling phase of the Minas Northwest Segment project successfully integrated all available interpreted and measured data into a high-resolution, full-field model. Statistical and geostatistical data revealed strong relationships between geologic trends and petrophysical patterns of reservoir heterogeneity. Facies regions combined with the powerful geostatistical methods in G2/GOCAD ++ effectively captured both geologic trends and petrophysical relationships in the final model. A scaled-up version of the geologic model was used to build streamtube and finite difference simulation models to predict recovery performance for various waterflooding and horizontal well scenarios. An inverted 7-spot pattern waterflood over a high-graded portion of the Northwest Segment was selected as the optimum development approach. The results and recommendations for this pattern waterflood project will be implemented beginning in 1998. The results derived from 3D scale-up and subsequent flow simulations showed that the geologic model used in this study adequately represents subsurface flow in Minas Northwest Segment. Introduction Minas field, located in central Sumatra, is the largest oil field in Southeast Asia (Fig. 1). The Minas field was discovered in 1944, and was placed on production in 1952. To date the field has produced more than 4 billion barrels from an original oil in place of 9 billion barrels. Minas field is a large (25km × 10km) faulted anticline separated into two major compartments (Minas Main and Minas Northwest) by a right lateral, strike-slip fault. The Northwest Segment represents approximately 20% of the Minas OOIP and is lower structurally than the Main Segment. The primary productive interval is within the Early Miocene Sihapas group. The sandstone reservoirs are at an average depth of 2000 feet subsea. P. 535^

This paper presents an improved new reservoir simulation model. By applying reservoir engineering, numerical simulation, and fluid flow theory through porous media to the conditions of production engineering, and taking into consideration the triple medium of matrix, natural fracture, and hydraulic fracture for tight low permeable fractured gas reservoirs, the authors arrived at a comprehensive conclusion for the low non-Darcy and high non-Darcy flows. A corresponding solution model is also presented, made available through computer programming. The results derived from the fracturing reservoir software, developed specifically in this study for tight low permeable fractured gas reservoirs and which takes into account such various aspects as the direction of and influence of in situ stress, reservoirroperties, natural fracture system, and hydraulic fracture length, show that the fracturing reservoir plan for the Xin Chang gas field has been worked out. The results prove that simulation softwarecan work as a powerful tool to design a fracturing reservoir lan for tight low permeable fractured gas reservoirs. Introduction Efforts to narrow the gap between investment and output, and to extend production while keeping recovery reasonably stable, encourages the wide use of fracturing reservoir techniques in gas and oil fields to maximize the economic benefits. The key to fracturing a reservoir is forecasting the best well net or post-fracturing production performance, which can provide reliable data for an adjustment plan for gas and oil fields. On the one hand, within tight low permeable fractured gas reservoirs, the fluid has to overcome the minimum pressure gradient to flow, which is characterized by low non-Darcy flow. On the other hand, the gas velocity is very high in hydraulic fracture, which is usually characterized by high non- Darcy flow. This study takes into account both the low non-Darcy flow and the high non-Darcy flow and puts forward an improved fracturing reservoir mathematical model for tight low permeable fractured gas reservoirs. Fracturing Reservoir Mathematical Model of Tight Clastic Gas Reservoir Based on successful numerical simulations in fractured reservoirs, and considering the triple medium of matrix, natural fracture, and hydraulic fracture, the fracturing reservoir simulationodel using three-dimensional (3D), two-phase (water and gas) triple medium for tight clastic gas reservoir was developed in which the influence of low non-Darcy velocity and high non-Darcy velocity on fluid flow in tight low permeable fractured gas reservoirs was originally studied at length. The model includes: Mathematical Model of High Velocity Non-Darcy Flow Basic Flow Equations Equation Available In Full Paper....(1) where Equation Available In Full Paper....(2) Coefficient of High Velocity Non-Darcy Flow Equation Available In Full Paper....(3) Mathematical Model of Low Velocity Non-Darcy Flow Equation Available In Full Paper....(4) General Mathematical Model of Non-Darcy Flow Equation Available In Full Paper....(5) when α= 0, β?= 1, the above equation represents linear Darcy flow; when α= 0, β= 0, the above equation represents high velocity non-Darcy flow; and, when α?= 1, β= 1, the above equation represents low velocity non-Darcy flow.

New Perspectives for 3D Visualization of Dynamic Reservoir Uncertainty

Reservoir characterization and flow prediction modeling form the fundamental basis of modern integrated Asset Management. As our oil industry evolves, increasingly sophisticated component technologies are being developed and applied in support of these characterization and modeling objectives. The oil industry is developing and embracing these new component technologies at a stunning pace - sometimes without full knowledge as to how each component might be consolidated into an overall solution to address real business issues. Forging through the maze of available technologies to identify a process which will address a specific business question is a challenge that requires several insights: an understanding of the business objective and risk, an understanding of specific details as to what each technology offers and, an understanding of how each technology can be linked to form an overall solution. This is a complex quest which often results in uncertainty within both company management and employees who are unclear as to how any individual technology will "pay out" or how a particular subprocess will be used as part of a whole to "make a difference". During ongoing efforts to evaluate new reservoir modeling technologies at Caltex, a team of Caltex specialists developed a combination work process/data flow model for reservoir modeling and characterization. The work/data process model definitions are expressed in the form of Reservoir Modeling Process (RMP) maps. These maps can be used to understand and define the overall objectives of an integrated team effort and accordingly allocate resources and project deliverables for Reservoir Characterization and Flow Modeling. The RMP process maps are scaleable in that various degrees of sophistication, and commensurate business risk-reduction, can be implemented. Examples of RMP application and scalability are offered from the historic use within Caltex CPI Indonesia. Current limitations and future evolution of the RMP process maps is also discussed.

As the role of reservoir flow simulation increasingly impacts existing operations and field development decisions, it follows that rigor, fitness and consistency should be imposed on the calibration of reservoir flow models to dynamic data through history matching. Although a wealth of history matching techniques exist in the petroleum literature that propose novel algorithms or share case studies, seldom does the content guide the modeler in fit-for-purpose reservoir model calibration for an operating asset. To evaluate the applicability of these diverse techniques against standards required for reservoir management, an internal study was performed to benchmark four assisted history matching (AHM) techniques commonly promoted in the oil and gas industry. The techniques were vetted against a comprehensive suite of modeling requirements for multiple asset classes, integrating a variety of historical dynamic data types through the calibration of reservoir properties that control flow behavior from the field to inter-well scale. The methods benchmarked were: (1) Design of Experiments (DoE)-based, (2) Ensemble Kalman Filter and Ensemble Smoother, (3) Genetic Algorithm and (4) Generalized Travel Time Inversion. This manuscript focuses solely on the DoE-based technique.In order to consistently benchmark the techniques, a set of standards was defined against which each was evaluated to determine its suitability for widespread history matching applications. The standards involve: the capacity to parameterize (and therefore calibrate) a diversity of reservoir flow model attributes, the capacity to integrate different types of dynamic data, the level of independence from the flow simulator and the capability to provide probabilistic outcomes for predictive uncertainty assessment. Of the four techniques, the DoE-based approach uniquely satisfied all requirements. Its history matching workflow has the flexibility to incorporate any form of reservoir model parameter and to assimilate a history matching error metric for any individual or group of historical data types; therefore, benchmarking established DoE-based techniques as unambiguously the most compliant with generic asset modeling requirements. The approach was also identified as the most straightforward, both theoretically and in practical computation, and therefore applicable to the broadest range of practitioners. Perhaps most importantly, the approach demonstrated the capacity for accurate quantification of uncertainty (or non-uniqueness) in reservoir quality resulting from an exhaustive, although approximate, exploration of model parameter space and the associated history matching error metric(s).This manuscript compiles the results and insights gained from benchmarking of the DoE-based techniques through the proposal of a comprehensive assisted history matching workflow. The workflow is designed for generality while providing best practices that guide the modeler in fit-for-purpose application. Limitations of the workflow are also recognized. Key components include: selection and screening of calibration parameters based on statistical significance, development of surrogate models to characterize the relationship between parameters and the simulated historical data being integrated into the reservoir model, use of the surrogate models for exhaustive yet efficient exploration of parameter space to identify (non-unique) history matched models, and (deterministic or probabilistic) discrete reservoir model selection for use in forecast-based decision making. Each step of the AHM workflow is presented from a conceptual and applied perspective, and field applications are provided to demonstrate key concepts. Although the applications presented include two deepwater Gulf of Mexico assets, the workflow and insights provided are developed from benchmarking across a diverse suite of asset types.

Significant uncertainty exists in the detailed 3-D distribution of lithofacies, porosity, and permeability in every reservoir. Understanding and modeling the heterogeneous 3-D distribution of these rock properties is critical for improved oil recovery and reservoir management. Geostatistical techniques are being increasingly used to generate alternative heterogeneous 3-D reservoir models that are consistent with the available data. Although a large number of stochastic reservoir models or realizations may be available, a small number of realizations are considered in practice. Due to computer limitations, it is only possible to visualize and perform fine-scale full-field flow simulation on a limited number of realizations. Techniques are reviewed in this paper for ranking a suite of geostatistical realizations so that low-side, expected, and high-side realizations may be reliably chosen. Detailed analysis/flow simulation may then be performed on these realizations that somehow bound the uncertainty in the reservoir. Reservoir management is improved when expected and bounding cases are considered rather than using a limited number of "random" realizations. This paper reviews a number of methods for ranking geostatistical reservoir models. These methods may be classified into three categories. The first category includes statistical methods such as simple statistics, 3-D measures of connectivity, and connectivity to specific well locations. Methods in the second class are based on approximations to flow simulation, e.g., random walk-type results. The third category is for flow-simulation based methods for a simpler process than that being considered for improved oil recovery, e.g., tracer simulation and flow simulation with coarsened models. The applicability of a number of ranking methods is illustrated with a small example. There is no unique ranking index when there are multiple flow response variables and no ranking measure is perfect. Nevertheless, the value of ranking realizations will be quantified by examining the expected loss knowing an economic loss function and the true distribution of uncertainty. Introduction The primary objective of the application of geostatistical tools for reservoir modeling is to create realistic numerical geological models or realizations of the 3-D spatial distribution of lithofacies, porosity. and permeability. An often disturbing fact of geological reservoir modeling is that there are alternative realizations that honor all of the available data equally well and yet yield different reservoir performance predictions. In fact, it is often advertised that "a different realization can be obtained by simply changing a random number seed". A fundamental principle of geostatistics is that of data integration, i.e., all known data should be honored "by construction" and not left to chance. Of course, this is not always possible; it is difficult to constrain detailed 3-D geological realizations to seismic and historical production data. We are restricting ourselves to plausible realizations. There are times when certain realizations would be rejected on the basis of familiarity with the reservoir or data not used in the geostatistical modeling such as production-related historical observations. We are not considering the rejects; only those realizations that meet all basic requirements of reasonableness are considered. Exact prediction / decision making would require exhaustive knowledge of the spatial distribution of porosity and permeability. At any specific instant in time, there is such a single true distribution of petrophysical properties. This true distribution was created by the complex interaction of many different chemical, physical. and biological processes over geological time and would be accessible only through exhaustive sampling. In all practical situations, the unique true distribution will remain unknown. This paper addresses the uncertainty due to incomplete information. We do not consider uncertainty due tothe volume support or scale difference between core measurements and geological modeling cells,the limited flexibility of our geostatistical modeling techniques to reproduce complex nonlinear features, ornumerical approximations in the subsequent flow simulator. P. 105

A 6M digital twin for modeling and simulation in subsurface reservoirs

Chapter 14 - Reservoir Flow Simulation

Time-lapse seismic (4D seismic) has emerged as a critical tool in understanding reservoir dynamics, particularly in complex deep-water turbidite sandstone systems. The application of 4D seismic in the Niger Delta's deep-water turbidite reservoirs has significantly enhanced reservoir management and further development, particularly in modeling waterflood fronts and identifying remaining and/or by-passed oil. This study explored the integration of 4D seismic data into reservoir performance history matching for modeling waterflood fronts and identifying bypassed or remaining oil. Deep-water turbidites are characterized by heterogeneous and high-permeability channel systems, leading to challenges in predicting waterflood efficiency and ultimate recovery. By combining 4D seismic with dynamic reservoir simulation, key insights into saturation changes (remaining oil), reservoir connectivity, and fluid flow pathways were obtained. This workflow involved processing an Ocean-Bottom-Node (OBN) 4D seismic data to extract changes in acoustic impedance, which were linked to saturation and pressure variations. These changes were then incorporated into the reservoir performance history matching to refine the reservoir model, ensuring better alignment with observed production and injection data. The seismic-derived waterflood front tracking enhanced the understanding of the reservoir sweep efficiency and flood front location, highlighting areas with bypassed oil due to intra-reservoir heterogeneities, stratigraphic barriers, sub-seismic faults, or suboptimal well placement. This integrated approach has been used in the Ufoma deepwater oilfield further development as a case study. It did not only improve the accuracy of the existing 3D Earth reservoir models but also supported the optimization of the field development strategy by successfully targeting the remaining oil pockets in the further development project for the reservoir. It also led to improved reservoir characterization and modeling in the Ufoma field reservoir management (i.e., WRFM - History Matching and Prediction) and has proven to be a valuable tool in maximizing hydrocarbon recovery in these complex geological channel sands bodies. This work further demonstrates the efficacy of this integrated approach, leading to improved well placement for maximum oil recovery and optimized field development strategies for the Ufoma oilfield in deepwater Niger-Delta.

A permeability model was developed for a thin valley-fill reservoir at Postle Field, Oklahoma, with the objective of increasing the accuracy of flow simulation. The presence of high-permeability zones that are below seismic resolution represented the greatest challenge to overcome. This challenge is common as the average reservoir thickness in North America is 17 ft (5 m). Enhanced oil recovery is underway at Postle Field to increase production and to revitalize the field. The process involves carbon dioxide flooding which is fast becoming the dominant mechanism of Tertiary recovery. To optimize the recovery efficiency of this process, it is important to understand the influence of permeability heterogeneity and its role on fluid flow. High-permeability zones have the potential to cause early breakthrough and poor sweep efficiency.