The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Data Science & Engineering Analytics
The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
The Libyan government has approved the sale of Hess' 8.16% stake in the giant Waha field to TotalEnergies and ConocoPhillips. Each company will add equal 4.08% stakes to their current 16.33% holdings in the project. Patrick Pouyanné, chief executive of TotalEnergies, confirmed the deal while speaking at the Libya Energy & Economic Summit this week. Financial terms of the transaction were not disclosed. A leaked letter dated 9 November 2021 showed that interim Prime Minister Hamid Dbeibeh welcomed and supported the proposed joint acquisition.
Abstract Machine learning has gained a substantial attention in recent years. Many Industries, including the oil and gas have adopted the technique in their applications. As of today, machine learning has been used in several aspects of petroleum engineering, from reservoir modeling and characterization to well placement optimization (Guyagulera et al. ,2002) and equipment malfunction predictions (Bangert, 2012). In petrophysics, machine learning has been used extensively as an alternative approach to conventional methods to classify rock facies based on available well data. This is generally because conventional methods involving visual examination of cores and assigning facies manually is a tedious and a time-consuming process. Nevertheless, most machine learning classification algorithms accuracy is reduced when the facies to be classified are not represented equally in the dataset i.e. the problem of data imbalance. In this paper, we compare the performance of five machine learning classification algorithms using an imbalanced data set where two facies types dominate the dataset. model selection is carried out first then algorithms are compared using cross-validation concept and finally best performing models are investigated further and compared in terms of prediction accuracy using the same data set. It is concluded that in an imbalanced dataset, simple support vector machines outperform the other four algorithms i.e. the tree-based algorithms and it is more efficient in predicting facies classes. Exploratory Data Analysis The data set used consist of seven well log measurements (referred to as features) from six wells in the Mabruk Field, southwest of Libya. Each set of measurements at a half-foot interval, is associated with a facies label (referred to as the target class). There are four distinct facies classes in the data set; Shallow Lagoon, Deep Lagoon, Shoal Lagoon and Shoal/Reef and these are labeled from 1 to 4 respectively. Distribution and count of each class per well is shown in figure (1a). We can clearly see that facies 2 and 4 dominate the data set and this might influence model predictability.
Abushalah, Yousf (University of Texas at El Paso) | Serpa, Laura (University of Texas at El Paso) | Abdelnabi, Abdalla (Missouri University of Science and Technology)
The Upper Cretaceous Waha Formation is the prime hydrocarbon reservoir in the RG Field in the central Sirte Basin, Libya. The porosity and permeability of the Waha carbonate reservoir vary widely from 5 to 20% and 0.01 to >2000 md, respectively, affecting the field performance in the study area. To understand the heterogeneity of the Waha reservoir properties, we first classified the reservoir facies based on their fabrics using a supervised neural network method. The classified facies which reflect the effect of depositional texture were then combined with the Xu-Payne rock physical model to investigate the effect of diagenetic and depositional processes and to predict the pore types. The results indicate that the reservoir characteristics are mostly facies dependent, and the classified facies based on rock fabric can be related directly to the reservoir properties. The high porosity and permeability of the Waha reservoir were found in grainstone facies that were characterized by interparticle porosity, while poor porosity and permeability in wackestone facies. The degradation of reservoir properties is predominantly related to the amount of mud matrix in the facies with a slight influence of compaction and cementation.
Masoud, Mohamed (Sirte Oil Company, Libya) | Meddaugh, W. Scott (Midwestern State University, Wichita Falls, TX) | Eljaroshi, Masoud (Sirte Oil Company, Libya) | Elghanduri, Khaled (Schlumberger)
Abstract The Harash Formation was previously known as the Ruaga A and is considered to be one of the most productive reservoirs in the Zelten field in terms of reservoir quality, areal extent, and hydrocarbon quantity. To date, nearly 70 wells were drilled targeting the Harash reservoir. A few wells initially naturally produced but most had to be stimulated which reflected the field drilling and development plan. The Harash reservoir rock typing identification was essential in understanding the reservoir geology implementation of reservoir development drilling program, the construction of representative reservoir models, hydrocarbons volumetric calculations, and historical pressure-production matching in the flow modelling processes. The objectives of this study are to predict the permeability at un-cored wells and unsampled locations, to classify the reservoir rocks into main rock typing, and to build robust reservoir properties models in which static petrophysical properties and fluid properties are assigned for identified rock type and assessed the existed vertical and lateral heterogeneity within the Palaeocene Harash carbonate reservoir. Initially, an objective-based workflow was developed by generating a training dataset from open hole logs and core samples which were conventionally and specially analyzed of six wells. The developed dataset was used to predict permeability at cored wells through a K-mod model that applies Neural Network Analysis (NNA) and Declustring (DC) algorithms to generate representative permeability and electro-facies. Equal statistical weights were given to log responses without analytical supervision taking into account the significant log response variations. The core data was grouped on petrophysical basis to compute pore throat size aiming at deriving and enlarging the interpretation process from the core to log domain using Indexation and Probabilities of Self-Organized Maps (IPSOM) classification model to develop a reliable representation of rock type classification at the well scale. Permeability and rock typing derived from the open-hole logs and core samples analysis are the main K-mod and IPSOM classification model outputs. The results were propagated to more than 70 un-cored wells. Rock typing techniques were also conducted to classify the Harash reservoir rocks in a consistent manner. Depositional rock typing using a stratigraphic modified Lorenz plot and electro-facies suggest three different rock types that are probably linked to three flow zones. The defined rock types are dominated by specifc reservoir parameters. Electro-facies enables subdivision of the formation into petrophysical groups in which properties were assigned to and were characterized by dynamic behavior and the rock-fluid interaction. Capillary pressure and relative permeability data proved the complexity in rock capillarity. Subsequently, Swc is really rock typing dependent. The use of a consistent representative petrophysical rock type classification led to a significant improvement of geological and flow models.
The study outlined in the complete paper focuses on developing models of the Upper Cretaceous Waha carbonate and Bahi sandstone reservoirs and the Cambrian-Ordovician Gargaf sandstone reservoir in the Meghil field, Sirte Basin, Libya. The objective of this study is to develop a representative geostatistically based 3D model that preserves geological elements and eliminates uncertainty of reservoir properties and volumetric estimates. This study demonstrates the potential for significant additional hydrocarbon production from the Meghil field and the effect of heterogeneity on well placement and spacing. The reservoir of interest consists of three stratigraphic layers of different ages: the Waha and Bahi Formations and the Gargaf Group intersecting the Meghil field. The Waha reservoir is a porous limestone that forms a single reservoir with underlying Upper Cretaceous Bahi sandstone and Cambro-Ordovician Gargaf Group quartzitic sandstone.
Total and Libya's National Oil Corporation (NOC) have completed a deal in which the French major will enter as a partner in the Waha concessions in the Sirte Basin of Libya. Total will invest $650 million in the development of two main projects, North Gialo and NC 98, resulting in a 180,000-B/D increase in production. In reaching those gains, Total will provide technology and expertise, NOC said. The French major in 2018 received a 16.33% working interest in six Waha concessions through the $450-million purchase of Marathon Oil Libya, a subsidiary of Marathon Oil. NOC said it approved Total's participation in the project as "no local partner has the technical or financial means to carry out the development of the concessions and to increase production."
Abstract Reliable and representative models and detailed characterization of a geologically complex reservoir are crucially important in having better understanding of the reservoir behavior and directly guiding to the most efficient implementation of the field development plan. This study focused on developing models of the Upper Cretaceous Waha carbonate and Bahi sandstone reservoirs and the Cambrian-Ordovician Gargaf sandstone reservoir in the Meghil field, Sirte Basin, Libya. The goals of the study were to characterize the vertical and lateral spatial continuity of each of the three formations and to calculate deterministic and probabilistic volumetrics. The Meghil Field, discovered in 1959, is located on the Zelten Platform and regionally classified as an extensional area of the larger Zelten field. Nineteen wells were drilled targeting the primary reservoir interval of interest at a drilled depth of around 8000 feet. A 3D seismic program was conducted to develop detailed structural maps for the Kalash and Waha/Bahi/Gargaf formations to evaluate future gas field development program. The field is characterized by three slightly asymmetrical anticlinal traps trending NW-SE. Major and minor faults that cut the interior of the structure were detected in the seismic block. The available drill stem DST and production tests were used to evaluate the level of communication between the structures. The structural framework for a 3D reservoir model is based on the interpretation and integration of the seismic volume and the available well logs. The well log data show that the net hydrocarbon bearing zone thickness is about 270 feet, the average porosity ranges from 4% in the Bahi/Gargaf sandstone to 13% in the Waha limestone. The average water saturation ranges from 15% to 32% in the Waha limestone and the Bahi/Gargaf sandstone respectively. Geostatistical models were developed using the well log and core data along with the structural model developed from the 3D seismic volume. The models suggest that porosity decreases towards the flanks and that separate flow units are likely present. The deterministic and stochastic give estimates of the original gas in place of about 830 Bscf and 732.2 Bscf for the upper and lower reservoir intervals, respectively. This study demonstrated the potential for significant additional hydrocarbon production from the Meghil field as well as the potential impact of heterogeneity on well placement and spacing.
Abstract Geostatistical-based models provide a considerable improvement for predictive reliability of dynamic models and the following reservoir management decisions. This study focuses on geostatistical modeling the Paleocene Zelten Carbonate reservoir in the Meghil field. The field was discovered in 1959 and production operations began in 1961. Nineteen wells have been drilled to date. The structural framework consists of three slightly asymmetrical anticlinal structures trending NW-SE with steeper dip on the SW flanks. Each of the structures are separated by major normal faults. Seismic interpretation suggests that carbonate build-ups are most likely present on the three separate structures. Edge detection was used to clarify the structural geometries and the presence of additional minor faults. Pillar gridding technique was used to develop the structural framework including four major faults that are partially sealed based on analysis of the available DST and production test data. Stratigraphic analysis indicates a local presentation of dolomitic limestone in the northern portion of the main and the western structures caused considerable litho-facies variation that impacted the distribution of the petrophysical properties. Basic and advanced formation evaluation the net reservoir thickness of about 15 feet with an average porosity of 17% and average water saturation of 35%. Geostatistical-based applications that combine the spatial statistics (e.g. the semivariogram) and the available well and core data were used to populate the reservoir model with porosity, permeability, facies (lithology), net/gross, and water saturation. A conceptual facies model was also used to constrain the reservoir property distributions. Sequential Gaussian Simulation (SGS) was used to populate the model with porosity and water saturation and Sequential Indicator Simulation (SIS) was used to populate the facies model with permeability. The modeling parameters (e.g. semivariogram, correlation coefficients) were significantly constrained by the limited number of wells. Based on the limited number of wells available the semivariogram analysis resulted in a spherical semivariogram model with major axis range of 1435 meters for porosity and 1800 meters for water saturation. Minor axis ranges were about 50% of the major axis ranges. Given the limited well data, a significant effort was made to document the potential impact of the semivariogram parameters on the original hydrocarbon in place (OHIP) estimates and the lateral stratigraphic continuity of reservoir properties. The deterministic approach resulted in place volume estimates of 60 MMBBL and the stochastic approach provided an estimate of 45 MMBBL.
Eni, BP, and Libya's National Oil Corporation (NOC) inked an agreement on 8 October that should enable Eni to buy a 42.5% stake and become operator of three of BP's Libyan oil exploration contract areas, where the companies plan to resume exploration work next year. BP's 54,000-sq-km exploration- and production-sharing agreement (EPSA) consists of two onshore contract areas, A and B, in the Ghadames Basin and one offshore area, C, in the Sirt Basin. BP currently holds an 85% interest in the EPSA, with the Libyan Investment Authority holding the remaining 15%. Eni and NOC jointly have other operations and infrastructure near the onshore areas. The companies plan to complete the deal by year end.
Abdelnabi, Abdalla (Missouri University of Science and Technology) | Liu, Kelly (Missouri University of Science and Technology) | Gao, Stephen (Missouri University of Science and Technology) | Abushalah, Yousf (Uinversity of Texas)
ABSTRACT Cambrian-Ordovician and Upper Cretaceous reservoir formations, the primary producing formations in the Sirte Basin, Libya have complex structures which affect the performance of the reservoirs. It is critical to understand the complicated relationships between fault networks, fractures, and stratigraphy of the area for future field development. However, detecting faults especially subtle faults and fractures is a challenging task using conventional seismic data due to the low signal-to-noise ratio. Seismic attributes provide effective tools in identifying and enhancing fault and fracture interpretation beyond the seismic resolution of the conventional seismic data. In this study, we focus on coherence and curvature attributes extracted from the post-stack 3D seismic data acquired in the central Sirte Basin to delineate subtle fault and fracture zones. We applied a median filter and spectral whitening to enhance the data quality and remove noise resulted from acquisition and processing effects. We utilized these methods to produce high-resolution data and preserve structural features. A total of 17 faults have been identified in the study area. The most common fractures in the Cambrian-Ordovician reservoir formations are in the northwest and southeast of the field. Seismic data conditioning and seismic attribute analyses applied on the 3-D seismic data effectively increased our understanding of the reservoir complex and help detect and identify subtle faults and fracture zones in the study area. Presentation Date: Tuesday, September 26, 2017 Start Time: 9:45 AM Location: Exhibit Hall C/D Presentation Type: POSTER