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Results
Research on Evaluation of Formation Water Salinity and Origin of Its Big Variation in Ultra-Low Permeability Clastic Reservoir
Feng, Cheng (Key Laboratory of Earth Prospecting and Information Technology, China University of Petroleum) | Mao, Zhiqiang (Key Laboratory of Earth Prospecting and Information Technology, China University of Petroleum) | Fu, Jinhua (PetroChina Changqing Oilfield Company) | Shi, Yujiang (PetroChina Changqing Oilfield Company) | Cheng, Yumei (Exploration Department, PetroChina Changqing Oilfield Company) | Li, Gaoren (PetroChina Changqing Oilfield Company)
Abstract For big variation of formation water salinity in Chang 8 stratum, Triassic, northwestern Ordos Basin, China, low resistivity contrast exists between oil layers and water layers. In order to increase the accuracy of log interpretation, accurate formation water salinity is a vital part. Based on the petrophysical theory, this paper summarizes and improves two methods to estimate formation water salinity. Firstly, reservoir resistivity-porosity cross plot method is introduced for oil-water layers and water layers. To be specific, resistivity and porosity log values of target reservoir are added to the cross plot. Data points, which are closest to the origin of coordinates, are selected as water layer ones. Then, formation water salinity is calculated by Archie formula. Secondly, shale water salinity is approximately regarded as formation water salinity. Because shale water salinity estimation is a nonlinear problem with small sample sets and there is no theoretical equation, Least Squares Support Vector Machine (LSSVM) is used for shale water salinity prediction. 9 parameters are extracted from lithology, resistivity and porosity log curves, among which, 5 are optimized as sensitive parameters by Principal Component Analysis (PCA). The effectiveness and reliability of resistivity-porosity cross plot and improved SVM method are tested by 23 formation water chemical analysis data. The average relative error of the former method is 19.79%, while that of the latter 27.57%. In addition, formation water salinity of another 50 wells are calculated by the two methods. Based on them, a salinity plane distribution map is drawn by Geomap software. In high salinity area, producing wells gather. Thus, one possible origin of formation water salinity variation is proposed. High salinity water moves into reservoirs with oil from source rock, which leads to high water salinity. In ultra-low permeability clastic reservoir with near source accumulation, formation water salinity probably varies significantly because of oil migration and accumulation. Furthermore, layers with oil often have higher formation water salinity, which is the main cause of low resistivity oil layers. Thus, the accurate formation water salinity calculated by the improved methods, will play an important role in the evaluation of low resistivity contrast oil layers and water layers.
- Asia > China > Shanxi Province (0.36)
- Asia > China > Shaanxi Province (0.36)
- Asia > China > Gansu Province (0.36)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (26 more...)
Abstract Seismic inversion approach has been applied with a moderate success in some siliciclastic reservoirs in Oriente Basin characterized by their prominent lateral facies variations. Different types of facies with different geological and petrophysical characteristics can produce similar response in seismic velocities. Based on this fact, inversion results such as acoustic impedance, S-Impedance, Vp/Vs., Poisson ratio and other derived elastic parameters can be misinterpreted leading to locating faked prolific zones in areas where facies do not have enough quality to be considered productive. In order to avoid this problem, rock physics analysis is used to tie the seismic inversion results to the geological and petrophysical rock characteristics. The Sun model has been proven to characterize successfully the seismic response of carbonate and siliciclastics rocks and infer geological, depositional, diagenetical and petro physical characteristics from sonic logs and seismic data. Using the compressional frame flexibility factor (ฮณk) and the shear frame flexibility factor (ฮณu) derived from this model, it is possible to successfully correlate geological, depositional and diagenetical characteristics to the seismic response using well log and core data from a siliciclastic reservoir in Oriente Basin. ฮณk and ฮณs factors characterize the influence of pore structure on the variability of compressibility and shear sonic velocities respectively in rocks. For the studied siliciclastic reservoir, which presents a prominent lateral variation in facies related to its depositional process, different ranges of ฮณk factor values represent different kind of facies. Values of ฮณk between 2 and 6 in "U" sandstone reservoir are closely correlated to the best quality facies. According to the integrated rock physical, petrophysical and geological analysis in well locations, clean massive sandstones with fine to medium grain size and moderately sorting are represented by this ฮณk value interval. Using simultaneous inversion results, volumes of porosity and ฮณk are inverted from seismic data. Spatial distribution of the ฮณk values in the inverted volume correlates very well with a previous sedimentological and stratigraphic interpretation study using core data descriptions. Finally, using porosity and values of ฮณk, the discrimination of the best quality facies is performed. As a final result, new prospective zones are visualized taking into account the structural characteristics and facies distribution obtained by this integrated analysis.
- Geology > Sedimentary Geology (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (1.00)
- South America > Ecuador > Nueva Loja > Oriente Basin > Block 15 > Eden Yuturi Field > Napo Field > Napo Formation (0.98)
- South America > Ecuador > Oriente Basin > Napo Formation > Napo U Formation (0.97)
- Asia > Middle East > Iraq > Wasit Governorate > Zagros Basin > Badra Field > P-14 Well (0.93)
- Asia > Azerbaijan > Aran Region > Middle Caspian Basin > Yevlakh-Aghjabady Depression > Muradkhanli-Jafarli-Zardab Block > Jafarli Field > C-21 Well (0.93)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic modeling (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Enhanced Core Analysis Workflow for the Geomechanical Characterization of Reservoirs in a Giant Offshore Field, Abu dhabi
Noufal, Abdelwahab (Abu Dhabi company for Onshore Petroleum Operations Ltd (ADCO)) | Germay, Christophe (EPSLOG SA.) | Lhomme, Tanguy (EPSLOG SA.) | Hegazy, Gehad (Abu Dhabi company for Onshore Petroleum Operations Ltd (ADCO)) | Richard, Thomas (EPSLOG SA.)
Abstract This paper is focused on the integration of two laboratory centimeter-resolution logs of mechanical properties (strength and compressional elastic-wave velocity Vp) into an enhanced core analysis workflow for the geomechanical characterization of unconventional reservoirs in a giant field in Abu Dhabi, where fracking is the cornerstone for producing the unconventional oil. The design and placement of hydraulic fratures rely strongly on the a-priori knowledge of the stress profile and brittleness index, which were estimated via a mechanical earth model constructed from wireline logs and correlations based on US shales analogues. With most of the stratigraphic column in the Abu Dhabi field composed of carbonates, the calibration of the mechanical earth models was found critical as the US shales based correlations would otherwise not have been suitable to the geomechanical characterization of these tight carbonate reservoirs. With this case study we illustrate:How the combination of the continuous profiles of rock strength UCS (Uniaxial compressive strength) and P-wave velocity measured directly on dry cores with the scratch tests contributes to the identification of different Geomechanical Facies, How the mapping of several Geomechanical Facies enables the building of a simple yet robust relationship between the UCS measured directly on cores and properties such as the total porosity and acoustic velocities of sonic waves, obtained from wireline logs, and How the centimeter-resolution profiles of strength and elastic wave velocities measured on dry cores enable the proper upscaling of geomechanical properties measured on plug samples to the entire cored section and the computation of a horizontal stress and brittleness profiles derived from unbiased geomechanical properties. From this case study follows a general discussion on the relevance of wireline sonic logs relative to centimetric resolution data (scratch profiles or plug measurement) acquired on dry cores for the geomechanical characterization of reservoirs. We conclude that measurements on dry cores enable the more robust calibration of mechanical earth model and in turn better description of the reservoir mechanical response. The upscaled profiles of horizontal stress and brittleness index derived from dry core measurements would ultimately lead to an alternative strategy for the design and placement of hydraulic fractures along producing wells.
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.55)
- Geology > Structural Geology > Tectonics > Compressional Tectonics > Fold and Thrust Belt (0.46)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- Asia > Middle East > UAE > Rub' al Khali Basin (0.99)
- Asia > Middle East > UAE > Abu Dhabi > Arabian Gulf > Rub' al Khali Basin > Abu Dhabi Field (0.97)
An Integrated, Multi-Disciplinary Approach Utilizing Stratigraphy, Petrophysics, and Geophysics to Predict Reservoir Properties of Tight Unconventional Sandstones in the Powder River Basin, Wyoming, USA
Zawila, Jeff (SM Energy) | Fluckiger, Sam (SM Energy) | Hughes, Gary (SM Energy) | Kerr, Preston (SM Energy) | Hennes, Andrew (SM Energy) | Hofmann, Michael (AIM Geoanalytics) | Wang, Haihong (CGG) | Titchmarsh, Howard (CGG)
Summary Numerous unconventional resources have become economically viable with the development of horizontal drilling and multi-stage hydraulic fracturing. Unconventional reservoirs have variable degrees of heterogeneity and identification of good and poor reservoir properties is essential for efficient development to define the economic limits of a resource play. An integrated, multi-disciplinary approach of correlating core facies to petrophysical wireline facies to seismic facies for tight unconventional sandstones is presented in this paper along with the results of a simultaneous, geostatistical seismic inversion. Seismic facies and reservoir rock properties, which are calibrated to wireline logs and core data, are mapped from 3D seismic inversion volumes. The maps provide a detailed understanding of the characteristics of the reservoirs, namely their spatial distribution, geometry, and internal architecture. This methodology demonstrates the tremendous value of incorporating stratigraphic, petrophysical, and geophysical data into a quantitative, integrated reservoir model. Introduction The Powder River Basin, located in northeastern Wyoming and southeastern Montana, USA (Figure 1) has produced conventional oil and gas since the 1890โs with the discovery of the Shannon and Salt Creek fields north of Casper (Roberts, 2015). Recent advances in horizontal drilling and multi-stage hydraulic fracturing renewed interest in the basin to test the economic viability of tight sandstone and carbonate resource plays. Since 2009, oil production in the Powder River Basin has increased 200% due to horizontal drilling mainly targeting the Turner/Wall Creek, Parkman, Niobrara, Sussex, and Shannon formations (US EIA, 2014). Methodology Seismic inversion is a tool to predict reservoir facies and properties away from calibrated well control. This technique has been successful in delineating the lateral extent and distribution of reservoir rock properties of conventional reservoirs. The same methodology is being applied to unconventional resource plays successfully as long as properly calibrated well control is available and seismic facies can be discriminated by acoustic and elastic parameters (Metzner and Smith, 2013; Goodway et al, 2012; Sena 2011).
- North America > United States > Wyoming (1.00)
- Asia > Middle East > Qatar > Arabian Gulf (0.24)
- Geology > Petroleum Play Type > Unconventional Play (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.97)
- Government > Regional Government > North America Government > United States Government (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- North America > United States > Wyoming > Powder River Basin > NPR-3 > Wall Creek Formation (0.99)
- North America > United States > Texas > Haynesville Shale Formation (0.99)
- North America > United States > Montana > Powder River Basin (0.99)
- (5 more...)
Abstract The drilling, completion, and stimulation of multiple fractured horizontal wells has proven to be an effective means of extracting hydrocarbons from unconventional resources. Since the first application of this technology by Maersk in the Dan Field in the mid-1980's oil and gas reservoirs have seen improved productivity and profitability. However, identifying the key drivers for success of multiple fractured horizontal well technology has proven difficult especially in unconventional reservoirs where data is limited. With few vertical wells, logs and core data typically used for building a basis of completion and stimulation design are often lacking. As a result, other methods of identifying success drivers must be developed and utilized. To this end, this paper utilizes a data-mining and statistical analysis of well, completion, fracture stimulation, and production data to establish the important parameters for success in horizontal wells in the Montney Formation of Alberta and British Columbia, Canada. In this study more than 3,300 horizontal wells were characterized with respect to lateral length, completion type, number of stages, fracture fluids pumped, proppant loading, costs, and production. The study utilized the statistical software JMP to identify key relationships between well data. The software system allowed standard screening and more advanced graphical methods to be applied to validate the dataset. From the quality assured dataset various additional parameters were calculated and used in the analysis. Both regression analysis and statistical โheat mapsโ were used to correlate and visualize data trends. Heat maps are shown as a useful tool for visualizing strongly trending data. Another finding from this study is that cased and cemented horizontal wells in the Montney Formation had significantly better initial productivity (+31%) and first year cumulatives (+42%) than open hole external packer completion systems even though the cased and cemented wellbores had fewer stages (-40%), larger stimulations (+390%), and increased costs (+14%). While additional completed stages may increase cumulative recovery in the Montney Formation, statistical analysis demonstrates the recovery per stage decreases after a certain stage density. This conclusion is consistent with recent findings (VISAGE and Jim Gouveia 2014). Results of the study clearly demonstrate that wells with the smallest frac fluid load recovery have the best cumulative gas recovery with time, and spending more for the completion translates into higher production. This work is important as it identifies relevant completion trends in the Montney Formation and completion and stimulation practices linked to higher recovery and well success. This is also the first field-wide statistical review of wells completed in the Montney Formation using more advanced data mining and statistical analysis. The work lays a foundation for application of these techniques to more unconventional and tight oil and gas reservoirs.
- North America > Canada > British Columbia (1.00)
- North America > Canada > Alberta (1.00)
- Europe > Denmark > North Sea > Danish Sector (0.24)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > Canada > British Columbia > Western Canada Sedimentary Basin > Alberta Basin > Montney Formation (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Montney Formation (0.99)
- (2 more...)
- Well Drilling > Drilling Operations > Directional drilling (1.00)
- Well Completion > Hydraulic Fracturing > Fracturing materials (fluids, proppant) (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Information Technology > Visualization (1.00)
- Information Technology > Data Science > Data Mining (1.00)
- Information Technology > Data Science > Data Quality (0.94)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Regression (0.34)
Abstract McElroy Field, located in the Permian Basin, is a typical example of a complex carbonate reservoir. Discovered in 1926, McElroy Field has been under waterflood since the early 1960's. However, maximizing oil recovery is still a major challenge in this field. A comprehensive analysis on the distribution of depositional facies and diagenetic modifications can ultimately enhance future development and oil production in the McElroy Field. We have applied a rock typing workflow based on conventional well logs and core data to incorporate both depositional and diagenetic attributes for characterizing the heterogeneity within the McElroy Field. The applied rock typing workflow consists of several sequential steps. Firstly, the depositional rock types were described and consolidated in the core domain for the purpose of propagation into the well-log domain. Reservoir typing was then conducted to identify controls on reservoir properties. This analysis indicated that diagenetic overprint has the dominant influence on the fluid flow in the McElroy Field. Pore type groups were classified by clustering attributes of Gaussian function fits to the pore-throat radius distributions derived from Mercury Injection Capillary Pressure (MICP) measurements. The identified depositional rock types and pore type groups were populated in the core-plug and the well-log domains applying a supervised model trained using k-Nearest Neighbors algorithm (KNN). Vuggy porosity was characterized in the core domain using CT-scan imaging techniques and correlated to log-derived estimates of porosity to predict vuggy porosity in the well-log domain. Assessment of vuggy porosity using CT-scan image analysis showed that the separation of sonic porosity and density-neutron porosity is not a reliable tool for estimating vuggy porosity in gypsum-bearing reservoirs. All of the generated geological and petrophysical data were integrated to define the petrophysical rock types that control the reservoir's dynamic characteristics. Identified petrophysical rock types were validated using dynamic injection profiles. The obtained results showed that the fluid flow in this field is dominantly controlled by diagenetic modifications. Finally, we studied the distribution of the identified petrophysical rock types to establish trends for field-wide spatial distribution of petrophysical rock types. The spatial trends of petrophysical rock types in the field serve to unveil the potential for future development opportunities in the McElroy Field.
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.46)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Strong Field (0.99)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- (31 more...)
Abstract New Zealand's geothermal systems contribute with over 14% of the national electricity supply. This share is projected to increase to 25% by the year 2025. To manage these resources optimally, a set of available modelling techniques developed over decades is used, providing means to integrate geological, geophysical, geochemical and engineering information. Current models, however, tend to simplify the geology of volcanic geothermal systems to satisfy computer simulator requirements. As a consequence, petrophysical properties at a gridblock scale are often calculated by calibration methods. Presently petrophysical data is scarce and the understanding of important properties such as porosity and permeability is limited. As an alternative, a rock typing technique based on textural features observed on hand samples to estimate reservoir quality is being applied. The methodology, developed to overcome the limited availability of drill-cores especially in exploration and early phases of development of petroleum reservoirs, is suitable for New Zealand's geothermal reservoirs where drill-cuttings are the main samples available for reservoir characterization. This paper presents a variation of textural descriptors used in sandstone and carbonate reservoirs, e.g., surface appearance, fabric, particle size, argillaceous content, visual porosity, and their application to volcanic rocks. Textural descriptors are combined in a classification method to produce rock types with similar reservoir quality. The optimal classification has been studied with results of an unsupervised neural network that evaluates the input data to find clustering patterns. This rock typing method has proven to be suitable to describe coherent lavas and volcaniclastic rocks in a geothermal reservoir. A catalogue of rock types that will provide analogues for comparison is under development, and a neural network is under training to provide means to propagate the classification between boreholes.
- Oceania > New Zealand (1.00)
- North America > United States > California (0.46)
- North America > United States > Texas (0.28)
- (2 more...)
- Geology > Geological Subdiscipline > Volcanology (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.35)
- North America > Canada > British Columbia > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin (0.99)
- Asia > Indonesia > Sulawesi > Tiaka Field (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin > Elmworth Field > Spirit River Formation > 764032 A-5 Gold Creek 6-4-68-4 Well (0.98)
- (2 more...)
Performance Step Change in Deviated Hole Enlargement While Drilling; Planning and Advanced Downhole Measurements Implementation in 17 1/2 X 20-in. and 14 3/4 X 17 1/2-in. Sections, Offshore Norway
Koberg, Ulrich (Statoil) | Harmer, Richard (Schlumberger) | Hassan, Haitham Khalil (Schlumberger) | Labrousse, Sebastien (Schlumberger) | Belmouloud, Hichem (Schlumberger)
Abstract The Gullfaks field was discovered in 1979 and has been developed via three production platforms. To meet objectives the well designs require 20-in. and/or 17 ยฝ-in. hole sections drilled to high angle (50ยฐ to 80ยฐ inclination), through the Utsira and Sotra formations and into the top of the Stord. The Sotra member within the Hordaland group, contains intervals of claystone and sandstone with a significant strength contrast, some thin intervals of dolomite may also be present. Performance under-reaming-while-drilling the 20-in. section has been variable, typically requiring between two to four bit runs due to cutting structure, drillstring and downhole tool failures. In an attempt to fully evaluate the downhole drilling environment, a new technology providing a comprehensive suite of advanced real-time and recorded-mode measurements of the drilling process was introduced. This information was used in the execution phase with remote interpretation support from drilling analysts. The higher resolution recorded data were analyzed in detail and used to calibrate models of the drilling process. The information obtained enhanced understanding of the Sotra formation and the downhole behavior of the drilling system. This increased understanding resulted in changes to operational procedures and bottom hole assembly design, which reduced the risk of drillstring and downhole tool failures. For the first time this hole section was drilled in one bit run, validating the feasibility of this approach, which was then transferred into the hole opening while drilling operations within a satellite field. Examples are included with novel insight into the response of under-reaming assemblies as the reamer encounters formation changes, an in-depth analysis of the static and dynamic loads placed on the bottom hole assemblies, and the impact of refinements to drilling procedures.
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 050 > Block 34/10 > Gullfaks Field > Statfjord Group (0.99)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 050 > Block 34/10 > Gullfaks Field > Lunde Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 050 > Block 34/10 > Gullfaks Field > Lista Formation (0.99)
- (3 more...)
Abstract This paper shows the importance of Artificial Intelligence (AI) techniques as a practical engineering tool for predicting and estimating the gas flow rate through chokes. Studying the single gas flow through wellhead chokes is vital to the oil industry, not only to ensure the accurate estimation of gas flow rate but also to keep equipments protected from damage due to high gas flow rate. It also has the potential to avoid sand problems. Many studies have investigated the predictability of gas flow through chokes. In this paper, we reviewed, evaluated and compared the predictive performance of the available choke correlations in literature with five AI techniques. 162 data points were used to develop five AI models for predicting the gas flow rate. The data were fed to the five AI techniques Artificial Neural Network (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Network (FN) and Decision Tree (DT) (ANN, FL, SVM, FN and DT) and the results were optimized for each technique. The new models were found to perform better than the correlation and give the lowest error, with a mean absolute percentage error of 0.83%. Because of these reduced errors, the proposed AI-based models can improve gas flow rate prediction through chokes. The results of this paper will provide a better alternative to predictive modeling of petroleum reservoir properties. It will also open windows of opportunity for researchers and engineers to explore advanced machine learning techniques such as hybrids and ensembles for continued improvement of petroleum exploration and production.
- North America (1.00)
- Europe (0.68)
- Asia > Middle East > Saudi Arabia > Eastern Province (0.28)
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > Sabriyah Field > Marrat Formation > Upper Marrat Formation (0.99)
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > Sabriyah Field > Marrat Formation > Sargelu Formation (0.99)
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > Sabriyah Field > Marrat Formation > Sabiriyah Mauddud (SAMA) Formation (0.99)
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > Sabriyah Field > Marrat Formation > SAMA Formation (0.99)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Fuzzy Logic (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)