Dhote, Prashant (Kuwait Oil Company) | Al-Adwani, Talal (Kuwait Oil Company) | Al-Bahar, Mohammad (Kuwait Oil Company) | Al-Otaibi, Ahmad (Kuwait Oil Company) | Chakraborty, Subrata (Schlumberger) | Stojic, Slobodan (Schlumberger)
Subsurface petroleum industry is burdened with uncertainties in every aspect from exploration to production due to limitations of accessibility to reservoir and technology. The most important tools used to understand, quantify and mitigate the uncertainties are geostatistical static modeling and numerical dynamic simulation geomodels. Geomodels are widely used in the industry for characterizing the reservoir and planning favorable development strategy. It is vital instrument for maximizing asset value and optimize project economics.
Static geomodels are foundation for all the advanced numerical and analytical solutions to solve the intricacies of reservoir performance. At the same time, it is where all the static and dynamic geological and engineering observations get integrated to develop common understanding of the reservoir for future studies. Understanding of the above observations and imaging of reservoir framework by individual is the basis for building static geomodels. Hence, at time, the process is highly subjective and proper QC'ing of the models to achieve the general and specific modeling objectives becomes imperative. Simple Questionaries’ based QC'ing and ranking methodologies are also controlled by subjectivity and individual preferences.
In the present endeavor, quantitative ‘Key Performance Indicators (KPIs)’ based standard static geomodeling practices and QC'ing methodologies at corporate level are developed in specially designed "Process Implementation Project (PIP) – Hydrocarbon resource and Uncertainty Management"’ under the aegis of ‘Kuwait Oil Company (KOC) - Reservoir Management Best Practices Steering Committee'.
The main objectives are to establish a practical modeling process, workflows and criteria to standardize modeling processes. A structured self-guidling modeling document has been developed with self-assemment guidelines and questionary. Finally, for each individual process a set of KPIs are specified as minimum standard to meet to obtain the approval of static model.
The present efforts are important for any geologists, geomodelers and reservoir engineers dealing with geostatistical and numerical reservoir modeling and will provide the KPI's based general practices for quality assurance (QA) and QC'ing of the models.
There is a critical and growing need for information about subsurface geological properties and processes over sufficiently large areas that can inform key scientific and societal studies. Airborne geophysical methods fill a unique role in Earth observation because of their ability to detect deep subsurface properties at regional scales and with high spatial resolution that cannot be achieved with groundbased measurements. Airborne electromagnetics, or AEM, is one technique that is rapidly emerging as a foundational tool for geological mapping, with widespread application to studies of water and mineral resources, geologic hazards, infrastructure, the cryosphere, and the environment. Applications of AEM are growing worldwide, with rapid developments in instrumentation and data analysis software. In this study, we summarize several recent hydrogeophysical applications of AEM, including examples drawn from a recent survey in the Mississippi Alluvial Plain (MAP). In addition, we discuss developments in computational methods for geophysical and geological model structural uncertainty quantification using AEM data, and how these results are used in a sequential hydrogeophysical approach to characterize hydrologic parameters and prediction uncertainty.
Presentation Date: Wednesday, October 17, 2018
Start Time: 8:30:00 AM
Location: 213B (Anaheim Convention Center)
Presentation Type: Oral
It is better to know what you don’t know than not know anything at all. For operators, this means using data analytics to understand shortcomings and successes in their own operations as well as competitors. Unfortunately, public data sources aren’t always maintained to the same standards as internal data, making field analysis difficult and accurate recommendations inconsistent or impossible. Leveraging multidisciplinary data analytics from raw public data such as digital well logs and production and completion data can help deliver necessary insights to understanding key successes and shortcomings of unconventional plays.
A case study of the Wattenberg field will be presented in this session, demonstrating why public data cannot be used in its raw format and the exponential value gained from a cross-discipline analytical process. Within the field, four geological horizons are targeted through horizontal drilling – the Niobrara (A-C Chalks) and Codell formations. The Niobrara consists of alternating chalk and marl units, whereas the Codell Formation consists of a clay-rich sandstone; both were deposited within the Interior Cretaceous Seaway. The petroleum system is overpressured and is believed to be self-sourced from the organic-rich marl intervals.
This study analyzed 1,100 digital well logs to generate a surface-based geological model that delineates where horizontal wells were drilled. In addition, completion and production data from over 4,500 wells were compiled, with type curves generated based on sub-region within the field, operator and vintage to normalize for geological variabilities. Highlights of this work include: geological parameters for optimal targets; differing estimated ultimate recoveries (EUR) on a lateral foot basis as operators transition away from the core of the play; optimal completion design; and changes in wellhead liquids percentages across the play. Results can be directly traced to conclusions such as higher proppant loading and longer well lateral lengths yield materially better well performance. In general, data accessed through public sources allows for larger sample sizes; however, it’s through a technically-sound methodology that the data can be analyzed at a granular level, illustrating the effectiveness of using a multidisciplinary approach.
The purpose of this paper is to present some alternative workflows and methods that can be used for a quick initial reservoir characterization. We apply these methods in different fields to analyze stratigraphic correlation, sand distribution, and recovery opportunities.
We analized Spontaneus Potential (SP) and Resistivity (R) logs, the only two curves run in the wells of the old mature fields studied. We built envelope curves for them, which are moving window averages that capture SP or R values for shales and clean sands. These curves are applied to compute the shale volume (Vsh) as an automatic multiwell process that takes into account formation and well variations in a simple way. We compared this Vsh with a traditional petrophysiscal interpretation and showed good validation results in many oilfields. We used this Vsh to build a simple sand-shale log and then a 3D model, analyzing spatial trends and connected volumes, which helped visualize main production units and waterflooded intervals. By means of simple calculations, it is also possible to determine non perforated hydrocarbon-bearing sands, and obtain a list of workover opportunities in a quick way. Zones for a possible EOR project could also be determined.
Additionally, we computed a derivative for the base envelope curve of R, which shows large scale variations in rock properties. We find vertical patterns in wells that can be followed for long distances, showing great lateral continuity, and helping determine a stratigraphic correlation between different oilfields. Although it has low resolution, it captures the main stratigraphic features.
Lastly, we computed a probabilistic forecast in 3D for each fluid type observed at swabbing tests. This is a routine workflow based on nearby swabbing tests, and should be simply used as a quick guide.
The envelope curves proved to be useful for a quick log normalization, baseline shift correction, Vsh calculation, and regional trend analysis. All these approaches are very effective in big fields with hundreds of wells and multiple layered reservoirs due to the speedy methodology. They are useful in visualization stages as an initial project overview.
Uncertainty assessment is a crucial step in any 3D geological modeling study, due to the fact that uncertainty exists in all input variables used to build the 3D geological model and due to its subsequent impact on the final in-pace hydrocarbon estimate. Experimental Design (ED) is well-suited to determine the main uncertain parameters, to evaluate the uncertainty impact on production forecasts, and to help accelerate decision making in reservoir development planning. The ED can be used in 3D modeling to minimize the number of model scenarios that are necessary to estimate hydrocarbon in place and predicate reservoir performance profile.
This paper describes in details the major steps of experiment design and propose an optimal procedure to conduct ED study. Topics covered includes how to defining key input variables, the different methods for selecting input distribution shape. Also, this paper covers the dependency between input parameters, ranking methods and results presentation. These concepts are applied on a clastic reservoir to illustrate the benefits of using the ED. Several cases are built using different input data with different distribution shape and dependencies. The results showed that the use of ED captured the potential uncertainty range while reducing the number of models realization needed. The approach is, also, very valuable in assessing the uncertainty of various inputs and their impact on the hydrocarbon in-place volumes.
Identification of channel geometry, facies boundaries, and characterization of channel petrophysical properties are critical for performance predictions of channelized reservoirs. Level set methods have shown great promise to effectively parameterize facies boundaries and allow for changing channel geometry and connectivity during history matching. An outstanding challenge is efficient updating of channel geometry as well as channel petrophysics during history matching. Also, seismic data can provide important information and needs to be used as constraints.
In this paper, a novel two-step history matching workflow is proposed where the channel geometry is modeled using level sets and the internal heterogeneity within the channel facies is modeled using parameterization with linear basis functions, specifically the Grid Connectivity Transform (GCT) basis. Facies boundaries are first represented by the level set function where seismic information is incorporated and the boundaries are gradually moved by solving the level set equation under the seismic constraints. For history matching, Markov Chain Monte Carlo (MCMC) method is employed to minimize production data misfit by adjusting channel geometry and channel petrophysics.
The proposed approach is applied to both 2D and 3D examples. First, we examine the effectiveness of the level set approach by comparison with other approaches for channelized reservoirs, such as Discrete Cosine Transform and Discrete Wavelet Transform. The level set approach is shown to outperform other methods significantly in terms of reproducing channel geometry. Second, we show that the use of seismic constraints helps preserve the structure of facies distributions and geologic realism during history matching. Finally, calibrated facies models are further updated by adjusting the internal channel permeability distributions to fine-tune the history matching. The permeability changes are carried out by perturbing the coefficients of the GCT bases. High and low permeability regions are clearly depicted within the channels and production data misfit is significantly reduced during this second stage. We demonstrate the power and utility of the approach using both 2D and 3D applications.
Previous approaches focused on conditioning channelized models to well data but seismic constraints in level set were not considered. The successful integration of seismic constraints can help not only improve channelized reservoir history matching performance significantly, but also extend applicability of the level set method from simple channelized models to more complicated ones. Also, the GCT approach, for the first time, is shown to capture internal heterogeneity of the channel architecture.
Ampomah, W. (Petroleum Recovery Research Center) | Balch, R. S. (Petroleum Recovery Research Center) | Ross-Coss, D. (Petroleum Recovery Research Center) | Hutton, A. (Petroleum Recovery Research Center) | Cather, M. (Petroleum Recovery Research Center) | Will, R. A. (Schlumberger Carbon Services)
This paper presents field scale reservoir characterization of the Farnsworth field unit undertaken as part of a Phase III project conducted by the Southwest Regional Partnership on Carbon Sequestration. Farnsworth Unit (FWU) is located on the northwest shelf of the Anadarko Basin. The target unit for CO2 injection, storage, and enhanced oil recovery (EOR) is an upper Morrow sandstone informally named the Morrow B Sand.
The Morrow B reservoir was deposited during early Pennsylvanian time as incised valley fill fluvial sands. Core and thin sections were examined to determine lithology, mineralogy, porosity types, depositional environment and diagenetic history. Data from XRD analysis, optical and scanning electron microscopy, and microprobe analysis were compared with results from an elemental log analysis (ELAN). This information, together with additional core, well log, borehole image log, vertical seismic profile and 3D surface seismic data was used to characterize and subsequently create a fine scale lithofacies-based geological model of the field. Structural modeling was based on integration of 3D seismic and compressional sonic well log data to create a velocity model convertin the seismic z-axis into the depth-domain. Converting domains allowed the 3D seismic data to be correlated to other depth-domain datasets, such as new and legacy well log data and core sections. Seismic attributes were able to illuminate previously unknown faults and structural elements within the field.
During the petrophysical modeling, several deterministic and stochastic techniques were compared and analyzed to ascertain which method best populates the geological properties into the model. These techniques included kriging, sequential Gaussian simulation (SGS) and Gaussian random function simulation (GRFS). A data analysis approach was used as a quality check to reduce uncertainty in the modeling. Studies of depositional environment provided data used constructing porosity—permeability crossplots; this proved to be a useful approach to assigning permeability to the lithofacies-based geological model. The accepted static model was upscaled for dynamic reservoir simulation.
The approach illustrated in this study presents an improved methodology in characterizing heterogeneous and complex reservoirs that can be applied to reservoirs with similar geological features.
Bourge, J. -P. (Schlumberger) | Bolaños, M. J. (Schlumberger) | Lafournère, J. -P. (Schlumberger) | Naranjo Leon, M. A. (Schlumberger) | Vega Torres, J. (Schlumberger) | Archard, G. (Schlumberger) | Suter, A. (Consorcio Shushufindi-Schlumberger) | Castillo, F. (Petroamazonas EP)
The main aim of geological modeling is to include all available data, such as seismic data, well logs, and core data, and to combine these data with more descriptive information, such as the geoscientist's understanding of the conceptual geological model and their experiences in similar environments, to predict the reservoir properties between the wells. However, in many cases, when the static model is passed to the reservoir engineer for history matching, the detailed geological knowledge and uncertainty is not fully utilized. This can lead to a model that may match the production data but actually has very little predictive power. Depositional maps provide very useful constraints on model building. By giving a visual representation of the geological context, they can incorporate well and seismic information and the dynamic characteristics recognized from the production data, tracer information in cases of water injection, and pressure information. However, there always remains a degree of uncertainty with respect to the geometries and orientations of the geobodies, so the tuning of the maps is, by definition, an iterative process. This coupling between static and dynamic modeling is critical to achieve true discipline integration, aiming to retain the key information from each domain. This paper presents an iterative technique to update these depositional maps in the areas of uncertainty between the wells.
Nilotpaul, Neog (Kuwait Oil Company) | Narahari, Srinivas Rao (Kuwait Oil Company) | Al-Darmi, Areej (Kuwait Oil Company) | Al-Dousiri Musaed, Yaseen Makki (Kuwait Oil Company) | Rawan, Hussain Al-Mayyas (Kuwait Oil Company) | Tom, De Keyzer (Technically Writes Consultancy) | Peter, Swart (CSL, University of Miami) | Kendall, Christopher G.St.C. (University of south Carolinian)
Field development in complex carbonate evaporite reservoirs has been a challenge for geoscientists by using simple sedimentology based geological models. A high resolution sequence stratigraphic framework has been designed from a conceptual 3D depositional model to a deterministic predictable model. In this process framework boundaries have been fine-tuned with carbon and oxygen isotope signals and defining ichno-facies associations to map lateral continuity of member formations within Marrat sequence. As dolomites and porous grain stones are the key reservoir quality rock types for Marrat Group of formations, position of precursor lithofacies in sequence stratigraphic frame work is found to be crucial. Dolomite samples are analyzed for d13C and d18O. Upper and Lower Marrat members are characterized by thinly laminated, micro crystalline exposure dolomites and high in d18O signal which are associated to low stand packages, whereas Middle Marrat dolomites are relatively low in d18O and high in d13C are associated to high stand precursor facies. Moreover, typical ichnofacies association of porous dolomites defining vertical heterogeneity and lateral connectivity of flow zones in Middle Marrat reservoirs. The reliability of this model has been established through recent tested intervals, with production logging and formation pressure tester data integration demonstrates predictability of flow zone connectivity.
Hull, David (Devon Energy, Oklahoma City, Oklahoma) | Chapman, Philip (Devon Energy, Oklahoma City, Oklahoma) | Miller, Dave (Devon Energy, Oklahoma City, Oklahoma) | Ingraham, David (Devon Energy, Oklahoma City, Oklahoma) | Fritz, Nicole (Devon Energy, Oklahoma City, Oklahoma) | Kernan, Nicholas (Devon Energy, Oklahoma City, Oklahoma)
A regional study of core from the eastern Karnes Trough area, outcrop, petrophysical data, literature, well logs, and other data was undertaken to evaluate the Eagle Ford in the Lavaca/DeWitt County area. This information was incorporated into 3D static earth models from which geologic inputs drive engineering testing and adaptations.
The modeling was grounded in a core study from multiple counties, which yielded six stratigraphic surfaces and eight facies between the top of the Upper Eagle Ford Marl and Buda Formation. These stratal surfaces guided the structural framework of the earth model, and the facies drove the 3D property models. The distribution and shape of these surfaces is primarily controlled by paleogeography, particularly the interplay of the San Marcos Arch, the Woodbine deltaic system, the Rio Grande Embayment, and the positions of the Sligo and Edwards Reef margins.
The eight facies grade from chalk/limestone to marl to clayey mudstones. In the modeling effort, the defined core facies were tied to well log signatures which were then used as predictive tools for facies in wells with no core data.
There are two key reservoirs in the system being modeled: the Lower Eagle Ford Shale, the dominant target in South Texas, and the more locally deposited Upper Eagle Ford Marl. The Lower Eagle Ford Shale reservoir facies is dominated by organic-rich, relatively low clay, foraminifera-rich, coccolith mudstones/marlstones. The Upper Eagle Ford Marl interval is extremely low in clay and rich in carbonate, composed of highly interbedded, organic-rich carbonate mudstone and bioclastic carbonate mudstone.
On the foundations of this geologic model, regional changes occurring in both reservoirs from DeWitt County to Lavaca County were investigated, including facies variability and other petrophysical/geologic properties.