Vahrenkamp, Volker (King Abdullah University of Science and Technology) | Khanna, Pankaj (King Abdullah University of Science and Technology) | Petrovic, Alexander (King Abdullah University of Science and Technology) | Ramdani, Ahmad (King Abdullah University of Science and Technology) | Putri, Indah (King Abdullah University of Science and Technology) | Sorrentino, Ranglys (King Abdullah University of Science and Technology)
The characterization and modelling of carbonate reservoirs can still be significantly improved to account for complex property and fracture network heterogeneities at scales difficult to resolve in the subsurface. The objective of this research is to develop and establish workflows for high fidelity geological modelling and characterization using modern and ancient carbonate outcrop analogues.
As a first step, we carefully selected high quality modern and ancient analogues to create comprehensive data sets on depositional heterogeneities. Advanced instrumentation and techniques were used such as 3D drone surveys, high-resolution surface geophysical surveys (50 MHz-100 MHz, and seismic), chirp sub- bottom profiler and high-resolution bathymetry mapping. These high-end techniques are paired with tried and tested standard geological techniques of measuring stratigraphic sections anchored by outcrop spectral gamma ray logs, analysis of sediment samples (texture, grain size, mineralogy, geochemistry) and fracture/fault surveys all integrated with full cores drilled in the outcrops. Using these, data models can be created for depositional and fracture heterogeneities at different scales and populated with ranges of property data like those found in actual reservoirs. The outcome will be a series of models for various carbonate reservoir settings and well location patterns with the goal of supporting drilling/exploration operations and reducing future development costs.
The project is based on two large-scale research projects of Jurassic carbonates outcropping in central KSA and a large modern carbonate platform in the Red Sea. Jurassic outcrops were analyzed using a unique dataset of measured sections including spectral gamma ray logs (300 vertical m), drone photogrammetry data (4x4 km2 overflight and several km's of vertical cliffs), seismic data (2 km), and GPR data (8 km). Data expose lateral heterogeneities, facies dimensions, and fracture networks at different scales. The modern carbonate outcrops are an ideal laboratory to investigate lateral facies heterogeneities and their relation to environmental factors influencing sediment distribution (prevailing winds versus storms, climate and nutrients). Around 800 km of hydroacoustic data, 50 sediment cores and 200 sea-floor samples were collected exposing significant and complex heterogeneities.
The outcome of these research projects significantly increases our understanding of property heterogeneity, facies distribution, fracture networks, and architecture of complex carbonate reservoirs. Resulting multi-scale modelling approaches and associated facies templates will improve the prediction of spatial heterogeneities of facies in subsurface reservoirs of similar settings. In addition, these datasets can be used as input for static analogue models and dynamic simulations to test sensitivities and determine optimum development scenarios for improving ultimate recovery.
Contreras Perez, David Rafael (OMV E&P GmbH - Abu Dhabi) | Al Zaabi, Ruqaya Abdulla (ADNOC Offshore - GUL) | Viratno, Bernato (OMV E&P GmbH - Abu Dhabi) | Sellar, Christopher (OMV E&P GmbH - Abu Dhabi) | Susanto, Maria Indriaty (OMV E&P GmbH - Abu Dhabi)
The rationale of structural uncertainty analysis in reservoir modeling is to quantify the range of probable Gross Rock Volume (GRV) s and searchfor the means to reduce this range as much as possible. This task considers running different scenarios and/or structural configurations based on the observed mismatch between structural depth estimation from seismic mapping and stratigraphic tops derived from well data. Integrated multi-disciplinary teams can collaboratively eliminate reservoir uncertainties at the well location, however uncertainty remains in the interwell area. The challenge for any reservoir characterization team is to share expertise across disciplines in order to mitigate the lack of information with scientific reasoning. In this way the range of uncertainties impacting business decisions, development scenarios or data acquisition plans are minimised. The workflow summarized here is an example of how to utilize structural elements from existing wells to quantify intrinsic GRV uncertainty while building static models. Offshore Field developments usually have a bigger horizontal well count than the ideal vertical penetrations and this case study is no exception in this case study. The ultimate goal of this publication is to generate the inputs required for a more realistic set of structural realizations that fulfil all of the current understanding from horizontal well placement and their intrinsic structural uncertainty.
A robust and detailed reservoir model is an essential requirement when a fast track approach drives the development of a green field. Such a tool can only be developed through the orchestration of Geological and Geophysical (G&G) and Reservoir Engineering disciplines. This integration effort is, first of all, aimed at identifying the key characteristics of the reservoir most impacting its dynamic behavior at different scale and, eventually, at capturing them with the proper modelling approach.
This paper decribes such approach to the case of a complex deep-water reservoir belonging to slope-toe of slope environment. A 3D integrated static model was built by incorporating core and log data, their petrophysical interpretation, a description of the depositional and architectural elements, a quantitative seismic reservoir characterization and the few dynamic information available at this early development stage.
The implemented geomodeling workflow focused on heterogenetiy that could affect reservoir performance such as structural-stratigraphic discontinuities that could act as hydraulic barriers. Facies in the interwell space were distributed by applying seismic-derived 3D trends. Facies distribution eventually provided the framework within which petrophysical properties modelling was performed. During the implementation of this integrated G&G and Reservoir workflow, continuous crosschecks of consistency and robustness of the model led to elaborate the final product.
The resulting reservoir model captured critical uncertainties (e.g. degree of reservoir heterogeneity including stratigraphic discontinuities) leading to an optimized development scheme, that allowed to minimize risks, despite the few data available.
With the increasing demand for hydrocarbons, unconventional reservoirs are gaining prominence and account for a large percentage of oil and gas production. However, these unconventional reservoirs inevitably include challenges that must be carefully managed while planning an extraction strategy to yield maximum recovery. This paper demonstrates the advantages of an integrated and automated well placement workflow to improve geosteering in complex unconventional reservoirs with maximum hydrocarbon recovery.
Automated well placement technique is controlled by three primary components: (1) an integrated asset model; (2) availability of uninterrupted, real-time log data; and (3) appropriately selected well planning methods. Initially, a dynamically updatable model of subsurface geology is created that combines surface topography, and an initial well trajectory is planned. As the well progresses, new log data are added to the asset model, and an interpretation is made in real time. Incorporating real-time data helps to dynamically update the model and enable a comparison of planned vs. actual deviation surveys for course corrections. This procedure guides the geosteerer to update well plans, run feasibility analyses, and predict subsurface uncertainties ahead of drilling, thus, increasing the reservoir penetration and overall well productivity.
Automated well placement while drilling is a relatively new concept and requires collaboration across various disciplines. Currently, such techniques are gaining importance among operators of unconventional resources as it enhances accuracy in well positioning and provides better production while reducing costs, drilling risks, and uncertainties. In addition, when targeting very thin, geologically complex reservoir layers, it provides a holistic view of the dynamically changing asset. The use of this approach will enable oil and gas operators to make collaborative, cross-domain decisions and streamline existing unconventional workflows.
Thorough reservoir modeling studies have been performed for field ABC, however there are still challenges to be addressed in modelling of some specific sand reservoir depositional systems i.e. meandering fluvial reservoirs (point bars and crevasse splays). The current modelling approaches especially for fluvial reservoirs are mainly controlled by wells and have contributed to uncertainties in lateral variation based on geostatistic (variograms etc) between and away from well control. Moreover, the existing modelling approach is using sixth to fifth order (lower order) hierarchical architecture elements and this project further refines the model up to third order (higher order) which enables capturing lateral accretion of point bars. Advanced fluvial workflow (AFW) have been developed to improve the understanding of the reservoir architecture of fluvial reservoirs. It comprises of three main steps which are, first, details study on fluvial reservoir sedimentology characteristics derived from core analysis and literature. Second, qualitative geophysical study and interpretation derived from seismic dataset.
Vorobev, Vladimir (Gazpromneft-GEO, LLC) | Safarov, Ildar (Gazpromneft-GEO, LLC) | Mostovoy, Pavel (Gazpromneft Science & Technology Centre, LLC) | Shakirzyanov, Lenar (Gazpromneft-GEO, LLC) | Fagereva, Veronika (Gazpromneft Science & Technology Centre, LLC)
Eastern Siberia is characterized by the extremely complex geological structure. The main factors include multiple faults, trappean and salt tectonics, the complex structure of the upper part of the section (0–1200 m) and its high-velocity characteristic (5000–6000 m/s), the high degree of rock transformation by secondary processes, low formation temperatures (10–30°C), the mixed fluid composition (gas, oil and water), and low net thicknesses (5–7 m) of productive layers. The fields of the region are among the most complex ones in the world according to the BP Company's statistics. New seismic and geologic model based on complex analyses of core, well logs, well tests, seismic and electromagnetic data allowed the Gazpromneft-GEO company to drill a series of successful wells.
Gazpromneft-GEO, LLC.holds three oil and gas exploration and production licenses within the Ignyalinsky, Vakunaisky and Tympuchikansky (Chona field) subsurface blocks (Russia, Eastern Siberia, Irkutsk Region and Republic of Sakha (Yakutia)). The area of the blocks is 6,855 sq.km, 3,050 sq.km of which are covered by the 3D seismic and high-density electric prospecting (
The work was carried out within the frames of scientific research and field works at the Gazpromneft-GEO, LLC. fields in Eastern Siberia. The high-density full-azimuth ground-based seismic using the UniQ technology was performed in Russia for the first time. The electric exploration with the near-field time-domain electromagnetic method was carried out along the same lines for the first time in Russia as well. This allowed to form the high-density cube of geoelectric properties. Model based on the wells (Facies model, Petrophysics model) and field geophysical data (3D seismic survey, 3D electric exploration, gravimetric survey, magnetic survey) complexation was made. The use of the approach allows to reduce the number of wells required for exploration of fields by 40%.
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.
Dommisse, Robin (University of Texas) | Janson, Xavier (University of Texas) | Male, Frank (University of Texas) | Price, Buddy (The University of Texas at Austin) | Payne, Simon (Ikon Science) | Lewis, Andrew (Fairfield Geotechnologies)
Modern reservoir characterization approaches can be greatly aided by incorporating all available data and interpretations in a three dimensional geomodel. Our goal is to offer a regional perspective to augment the interpretations from local, field-scale 3D models developed by the industry. In this work we highlight the benefits of continuous development of the geomodel for the characterization of the facies architecture of an unconventional play.We generated a three dimensional, faulted Delaware Basin geomodel, containing over 1 billion cells, including stratigraphic, petrophysical, core description, and production data for the Bone Spring and Wolfcamp intervals. The model is based on over 7,000 correlated wells, 650 wells with facies interpretations and approximately 9,000 horizontal production wells with analyzed decline curves and completion data. Additionally, a high-quality 3D seismic volume in the northeastern part of the Delaware Basin reveals the complex stratigraphic architecture of key producing intervals in the Permian Basin. The 3D volume, combined with regional 2D seismic lines, enabled refining the interpretation of the stratigraphic architecture of the Wolfcampian to Guadalupian shelf margin. This allows us to relate the slope to basin strata imaged in the 3D seismic to the well-established stratigraphic architecture of the surrounding platforms. The 3D seismic volume reveals the seismic geomorphology of several key intervals. There are two areas of focus: 1) Testing of the facies model derived from log and core analyses using different deterministic and stochastic attribute distribution techniques; and 2) Exploring the influence of geological trends on productivity. This work demonstrates the value of a multiscale, regional perspective to the practice of 3D reservoir characterization in the Delaware Basin.
Identification of a prospect is normally done based on seismic interpretation and geological understanding of the area. However, due to the inherent uncertainties of the data we still observe in many cases that all key petroleum system elements are present, but still the drilled prospect is dry. Such failures are mostly attributed to a lack of understanding of seal capacity, reservoir heterogeneity, source rock presence and maturation, hydrocarbon migration, and relative timing of these processes. The workflow described in this paper aims to improve discovery success rates by deploying a more rigorous and structured approach. It is guided by the play-based exploration risk assessment process. The starting point is always that the process is guided by the the basic understanding of a mature kitchen should always be based on a regional scale petroleum systems model. However, while evaluating prospects, the migration and entrapment component of a prospect should always be investigated by means of a locally refined grid-based petroleum system model. The uniquepart of this approach is the construction of a high-resolution static model covering the prospects, which is built by using available well data, seismo-geological trends and attributes to capture reservoir potential. Additional inputs such as fault seal analysis also helps to understand prospect scale migration and associated geological risks. In the regional play and local prospect-scale petroleum system models, geological and geophysical inputs are utilized to create the uncertainty distribution for each input parameter which is required for assessing the success case volume of identified prospects. The evaluated risk is combined with the volumetric uncertainty in a probabilistic way to derive the risked volumetrics. It is further translated into an economic evaluation of the prospect by integrating inputs like estimated production profiles, appropriate fiscal models, HC price decks, etc. This enables the economic viability of the prospects to be assessed, resulting in a portfolio with proper ranking to build a decision-tree leading to execution and operations in ensuing drilling campaigns.