Saputelli, Luigi (ADNOC) | Celma, Rafael (ADNOC) | Boyd, Douglas (ADNOC) | Shebl, Hesham (ADNOC) | Gomes, Jorge (ADNOC) | Bahrini, Fahmi (Frontender Corporation) | Escorcia, Alvaro (Frontender Corporation) | Pandey, Yogendra (Prabuddha)
Permeability and rock typing are two of the main outputs generated from the petrophysical domain and are particularly contributors to the highest degree of uncertainty during the history matching process in reservoir modeling, with the subsequent high impact in field development decisions. Detailed core analysis is the preferred main source of information to estimate permeability and to assign rock types; however, since there are generally more un-cored than cored wells, logs are the most frequently applied source of information to predict permeability and rock types in each data point of the reservoir model.
The approach of this investigation is to apply data analytics and machine learning to move from the core domain to the log domain and to determine relationships to then generate properties for the three-dimensional reservoir model with proper simulation for history matching. All wells have a full set of logs (Gamma Ray, Resistivity, Density and Neutron) and few have routine core analysis (Permeability, Porosity and MICP). On a first pass, logs from selected wells are classified into Self Organizing Maps (SOM) without analytical supervision. Then, core data is used to define petrophysical groups (PG), followed by linking the PG's to NMR pore-size distribution analysis results into pre-determined standard pore geometry groups, in this step supervised PGs are generated from the log response constrained by the relationship between pore-throat geometry (MICP) and pose-size distribution (NMR). Permeability-porosity core relationships are reviewed by sorting and eliminating the outliers or inconsistent samples (damaged or chipped, fractures or with local features). After that, the supervised PGs are used to train and calibrate a supervised neural network (NN) and permeability and rock type's relationships can be captured at log scale. Using dimensionality reduction improves the neural network relationships and thus data population into the petrophysical wells.
The result is a more robust model capable to capture over 80% of the core relationships and able to predict permeability and rock types while preserving the geological features of the reservoir. The application of this method makes possible to determine the relevance of core and log data sources to address rock typing and permeability prediction uncertainties. The applied workflows also show how to break the autocorrelation of variables and maximize the usage of logs.
This work demonstrates that the introduced data-driven methods are useful for rock typing determination and address several of the challenges related to core to log properties derivation.
This seminar will teach participants how to identify, evaluate, and quantify risk and uncertainty in everyday oil and gas economic situations. It reviews the development of pragmatic tools, methods, and understandings for professionals that are applicable to companies of all sizes. The seminar also briefly reviews statistics, the relationship between risk and return, and hedging and future markets. Strategic thinking and planning are key elements in an organisation’s journey to maximise value to shareholders, customers, and employees. Through this workshop, attendees will go through the different processes involved in strategic planning including the elements of organisational SWOT, business scenario and options development, elaboration of strategic options and communication to stakeholders.
Green fields today mostly can be regarded as marginal fields and successfully developed. It covers the complete assessment of the oil and gas recovery potential from reservoir structure and formation evaluation, oil and gas reserve mapping, their uncertainties and risks management, feasible reservoir fluid depletion approaches, and to the construction of integrated production systems for cost effective development of the green fields. Depth conversion of time interpretations is a basic skill set for interpreters. There is no single methodology that is optimal for all cases. Next, appropriate depth methods will be presented. Depth imaging should be considered an integral component of interpretation. If the results derived from depth imaging are intended to mitigate risk, the interpreter must actively guide the process.
Decisions in E&P ventures are affected by Bias, Blindness, and Illusions (BBI) which permeate our analyses, interpretations and decisions. This one-day course examines the influence of these cognitive pitfalls and presents techniques that can be used to mitigate their impact. Bias refers to errors in thinking whereby interpretations and judgments are drawn in an illogical fashion. Blindness is the condition where we fail to see an unexpected event in plain sight. Illusions refer to misleading beliefs based on a false impression of reality.
Reservoirs and the lateral seal of stratigraphic traps are controlled by the depositional environment or diagenesis. The recognition of facies and lithology from seismic attributes is an effective approach for identifying stratigraphic traps related to the depositional environment. In this paper, the occurrence of stratigraphic traps related to depositional environment in Permian aeolian clastics and Jurassic carbonate-evaporites was studied. To identify these stratigraphic traps, multiple seismic attributes were classified using supervised and unsupervised artificial neural networks (ANNs), which allowed the recognition of seismic facies and lithology.
Neural networks are a powerful classification technique, which incorporates multiple attributes into a number of classes to identify sedimentary facies. Two algorithms comprising supervised and unsupervised neural networks are commonly implemented. With a supervised learning algorithm, prior information such as typical facies at the control wells are required to train the multilayer perceptron (MLP) network. With an unsupervised algorithm, only seismic data is input to the neural network, and competitive-learning techniques are employed to classify or self-organize the data based on its internal characteristics. Without prior information, the output classes are not labeled with lithofacies. According to the availability of prior information, supervised and unsupervised learning were applied to recognize dune-playa and carbonate-evaporite combinations, respectively. To characterize the depositional environments, joint interpretation with a geological model is necessary for both supervised and unsupervised classification.
Two major findings have been derived from this work. First, the learning technology based on ANNs is effective to recognize sedimentary facies. The microfacies and lithologies identified by both supervised and unsupervised ANNs are very consistent with the drilled wells. Second, the recognition of depositional facies and lithology can characterize the stratigraphic traps in the study areas. Lateral seal plays a key role in stratigraphic traps. Playa siltstone and tight lagoonal limestone constitute the lateral seal in dune-playa and carbonate-evaporite combinations, respectively.
Techniques for 3D seismic interpretation by geoscientists are continuously undergoing improvements, and future exploration is anticipated to continue to benefit from high-confidence first pass interpretations utilizing all of the available seismic and well data. Workflows have been developed on a'super-merge' 3D volume to produce attribute-enhanced chronostratigraphic stratal surfaces, allowing interpretation of regional-scale seismic facies and associated seismic geomorphology and tectonostratigraphy. In this example, a semi-supervised machine-based learning workflow has provided rapid turnaround interpretation of the structural framework and chronostratigraphy throughout the entire 3D seismic volume, maximizing the value of the seismic information. This workflow consists of a three-step auto-tracking workflow to build a Relative Geological Time (RGT) geo-model directly from the seismic volume. This enables more time to spend on geological validation and interpretation of the stratal surface seismic geomorphology. Study results have provided the foundation for rapid turnaround well and seismic integrated play fairway maps; a powerful tool for stimulating exploration in mature areas or wildcat acreage assessment. This study focused on Middle and Upper Jurassic carbonates deposited on a broad low angle platform on the Arabian Plate. Interpreting in map view on RGT constrained stratal surfaces with attributes such as, relative acoustic impedance and spectral decomposition, is invaluable for visualization since the stratal surface follows the morphology of the imaged geologic features. The ability to select any stratal surface within the volume and flatten, either on a seismic display or the Relative Geological Time geo-model, is particularly useful to establish the timing of major tectonic episodes and accommodation space fluctuations.
Hassan, Ahmed (King Abdullah University of Science and Technology) | Chandra, Visawanthi (King Abdullah University of Science and Technology) | Yutkin, Maxim P. (King Abdullah University of Science and Technology) | Patzek, Tadeusz W. (King Abdullah University of Science and Technology) | Espinoza, D. N. (University of Texas at Austin)
Microporous carbonates contain perhaps 50% of the oil left behind in current projects in the giant carbonate fields in the Middle East and elsewhere. Pore geometry, connectivity, and wettability of the micropore systems in these carbonates are of paramount importance in finding new improved-oil-recovery methods. In this study, we present a robust pore-imaging approach that uses confocal laser scanning microscopy (CLSM) to obtain high-resolution 3D images of etched epoxy pore casts of the highly heterogeneous carbonates. In our approach, we have increased the depth of investigation for carbonates 20-fold, from 10 µm reported by Fredrich (1999) and Shah et al. (2013) to 200 µm. In addition, high-resolution 2D images from scanning electron microscopy (SEM) have been correlated with the 3D models from CLSM to develop a multiscale imaging approach that covers a range of scales, from millimeters in three dimensions to micrometers in two dimensions. The developed approach was implemented to identify various pore types [e.g., intercrystalline microporosity (IM), intragranular microporosity (IGM), and interboundary sheet pores (SPs)] in limestone and dolomite samples.
Retrofitting separators with internals can be considered to be a five-step process. What are you trying to accomplish, e.g., improve flow distribution, reduce droplet shearing, increase gas capacity? Supports. How to support the internals? Alternative supports such as expansion rings? How to prepare site/vessel (e.g., isolation, cleanout) and actually install the internals and supports once they are fabricated.
BinAbadat, Ebtesam (ADNOC Offshore) | Bu-Hindi, Hani (ADNOC Offshore) | Lehmann, Christoph (ADNOC Offshore) | Kumar, Atul (ADNOC Offshore) | AL-Harbi, Haifa (ADNOC Offshore) | AL-Ali, Ahmed (ADNOC Offshore) | Al Katheeri, Adel (ADNOC Offshore)
In this study, core and log data were integrated to identify intervals which are rich in stromatoporoids in an Upper Jurassic carbonate reservoir of an offshore green field Abu Dhabi. The main objective of this study was to recognize and stromatoporoids floatstones/rudstones in core, and develop criteria and workflow to identify them in uncored wells using borehole images.
The following workflow was used during this study: i) Identification of the stromatoporoid feature in pilot wells with core and borehole images, ii) Investigate the properties and architecture of stromatoporoid bodies, iii) Integrate the same scale of core observations with borehole images and conventional log data (gamma ray, neutron porosity and bulk density logs) to identify stromatoporoid-rich layers, iv) Performing a blind test on a well by using the criteria developed from previous steps to identify "stromatoporoid accumulations" on a borehole image, and validate it with core observations.
In the reservoir under investgation, stromatoporoid floatstones/rudstones intervals were identified and recognized both on core and borehole image in the pilot wells. These distinct reservoir bodies of stromatoporoids commonly occur in upper part of the reservoir and can reach to a thickness of around 20ft. The distribution and thickness of stromatoporoid bodies as well as growth forms (massive versus branching) were recognized on core and borehole images. The accumulations varied between massive beds of containing large pieces of stromatoporoids and grainstone beds rich in stromatoporoid debris. The massive beds of stromatoporoid accumulations are well developed in the northern part of the field. These layers can enhance the reservoir quality because of their distinct vuggy porosity and permeability that can reach up to several hundred of milidarcies (mD). Therefore, it is important to capture stromatoporoid layers both vertically and laterally in the static and dynamic model. Integrating borehole image data with core data and developing a workflow to identify stromatoporoid intervals in uncored wells is crucial to our subsurface understanding and will help to understand reservoir performance.
Integration of image log data which is calibrated to core and log data proved to be critical in generating reservoir facies maps and correlations, which were integrated into a sequence stratigraphic framework as well. The results were used in the static model in distribution of high permeability layers related to the distribution of stromatoporoids.
Goraya, Yassar (Adnoc Offshore) | Nair, Rajeev Nair (Fugro) | AL-Neaimi, Ahmed Khalifa (Adnoc Offshore) | AL-Felasi, Ali (Adnoc Offshore) | Kleef, Franciscus Johannes (Adnoc Offshore) | Al-Dhafari, Bader (Adnoc Offshore) | El-Sayed, Mohamed Abdul-Khalek (Adnoc Offshore) | Akram, Fazeel (Adnoc Offshore) | AL-Hosani, Ibrahim Ali (Adnoc Offshore)
During a routine tower maintenance visit, gas bubbles were observed at sea bed. The challenge now was to identify the source of the gas leak and identify areas where gas had accumulated. The observed gas seep, escaped from the seabed to the water column and was in the vicinity of the TWR-2 platform as confirmed during a diving survey. A geophysical survey was initiated to understand if gas had accumulated in the subsurface and whether it was safe to approach the site with a rig to kill the well.