Bian, Changrong (Sinopec Exploration & Production Research Institute) | Zhang, Dianwei (Sinopec Exploration & Production Research Institute) | Shen, Feng (GeoReservoir Research) | Wo, Yujin (Sinopec Exploration & Production Research Institute) | Sun, Wei (Sinopec Exploration & Production Research Institute) | Li, Jingliang (GeoReservoir Research) | Han, Juan (GeoReservoir Research) | Li, Shuiquan (GeoReservoir Research) | Ma, Qiang (Sinopec Exploration & Production Research Institute)
Delineating geometry of natural fractures realistically and understanding fracture stress sensitivity help to optimize well placement and well spacing design in shale gas reservoirs. This paper presents a methodology for building 3D hybrid discrete natural fracture network (DFN) models and using an analytical model to assess reactivation potential of natural fracture in the Longmaxi shale, Sichuan Basin.
Small-throw faults and natural fractures ranging from seismic scale to well scale in shale reservoirs have important effects on the success of horizontal drilling and hydraulic fracturing. Seismic geometric multi-attributes at different resolution scales are used to classify seismic facies according to the degree of fracturing. Small-throw faults are delineated using seismic facies and validated against drilling data. We develop a discrete natural fracture network (DFN) model at the seismic scale by meshing fracture lineaments tracked from an enhanced curvature attribute. Fracture topologies are used for fracture connectivity analysis to build local fracture networks along and around the horizontal wellbores. Diffuse fractures at the small scale are modeled with curvature attributes and well data analysis under the constraint of the seismic facies. The analytical model incorporates fracture properties and geomechanical model to describe the deformation of natural fractures due to hydraulic fracturing. Fracture stress-sensitivity are assessed based on changes of fracture volumes under different stress conditions. Characterized reactivated local fracture networks at different scales along the horizontal wells are used to map out volumetric extent of zones with potential to develop tensile and shear deformation during hydraulic fracturing. Available microseismic data from the hydraulic fracture stimulation of the reservoir is used to validate the fracture models.
Our stress sensitivity analysis indicates that reactivation potential of natural fractures varies considerably, mainly depending on natural fracture size and orientation, rock mechanical properties and anisotropy of horizontal stresses. DFN models reveal that fracture concentrations are correlative with the footprint of observed microseismic events. Comparison of 3D natural fracture models with the microseismic event distribution shows that vertical variation of fracture properties in the laminated shale reservoir adds complexity for fracture propagation.
A case study is used to illustrate the efficiency of the methodology. Fracture models at different scales and associated fracture stress-sensitivity can be used as a predictive tool for locating new wells and completion design in shale gas reservoirs.
Estimating the lateral heterogeneity of geochemical properties of organic rich mudrocks is important for unconventional resource plays. Mature regions can rely on abundant well data to build empirical relationships and on traditional geostatistical methods to estimate properties between wells. However, well penetration in emerging plays are sparse and so these methods will not yield good results. In this case, quantitative seismic interpretation (QSI) might be helpful in estimating the desired properties. In this study, we use QSI based on a rock physics template in estimating the uncertainty of the geochemical properties of organic mudrocks of the Shublik Formation, North Slope, Alaska. A rock physics template incorporating lithology, pore fraction, kerogen fraction, and thermal maturity is constructed and validated using well data. The template clearly shows that the inversion problem is non-unique. Inverted impedances cubes are estimated from three seismic angle gathers (near with angles between 0° and 15°, mid with angle gathers between 15° and 30°, and far with angle gathers between 30° and 45°). The inversion is done using a model-based implementation with an initial earth model derived from the seismic velocity model used in the processing phase. By combining the rock physics template and the results of seismic inversion, multiple realizations of total organic content (TOC), matrix porosity, and brittleness index are generated. These parameters can be used for sweet spot detection. Lithological results can also be used as an input for basin and petroleum system modeling.
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.
Historically, acoustic wellbore monitoring is one of the main methods of detecting fluid movement behind the casing. Analysis of the complex acoustic environment in the wellbore can be challenging. A standard hydrophone noise tool is unable to measure flow directions (vertical and horizontal) and cannot detect low flow, low pressure sporadic events or multiple sources. This uncertainty may result in subjective acoustic interpretation leading to poor advice on remedial actions for wells with well integrity issues. A geophone array, including four 3-component geophones deployed via Wireline, provides a solution for this problem by creating a three-dimensional map of the acoustic environment. This acoustic profile, with accurately measured background noise levels throughout the length of the well, is then analyzed to confirm source locations and presence of flow (gas / oil / water) behind casing. The Unique geophone array configuration has allowed us to confirm source locations and flow paths of unwanted fluid flow for hundreds of conventional and unconventional production wells. Scenarios include wells with surface-casing or annular vent-flow issues, multiple source vent-flow situations, wells with cross flow between zones and integrity confirmation for gas storage caverns / zones. Since the detected flow is rendered into horizontal and vertical components, we can determine flow direction and accurately pin-point depth levels of fluid entry from the formation into the wellbore annulus. Geophone array noise logging has occurred in approximately 500 wells globally and the success rate for first attempt repairs is about 80% which is significantly higher than the typical 30% success rate.
Tian, Wenyuan (CNODC, Author is also affiliated with BGP) | Jia, Minqiang (BGP) | Xiao, Dengyi (BGP) | Luo, Beiwei (REPID) | Yang, Jianfang (BGP) | Al Suwaidi, Saeed K. (Al Yasat) | Ji, Yu (University of Southern California) | LV, Mingsheng (CNODC) | Shashanka, Ashis (Al Yasat) | Mao, Demin (BGP) | Hu, Xinli (BGP)
Unconventional studies of UAE is in the early stage, especially in the western part, where the wells were drilled only for conventional oil & gas, with few unconventional data acquired. Shilaif formation is one of the main source rock of Cretaceous in western UAE. The main lithology of Shilaif are argillaceous limestone, lime mudstone and shale. Source rock geochemical analysis and basin modeling studies of western UAE show good source rock of Shilaif with high TOC, large thickness and high maturity mainly distribute in the southeast part of the study area, which is the high potential area for unconventional oil exploration. Based on the available 3D seismic data and log data, a series of techniques were used to predict the sweet spots of unconventional oil of Shilaif source rock, which includes the following main techniques: 1. 3D Seismic CRP Gather Conditioning; 2. Petrophysics Modeling; 3. Pre-stack Inversion; 4. Fracture Prediction; 5. Hydrocarbon Prediction; 6. Pore Pressure Prediction; 7. In-Situ Stress Analysis. Based on these techniques, one SW-NE belt of sweet spots were predicted in Lower Shilaif formation of the study area, with thick good source rock, high oil retention, high brittleness, high pressure, medium fracture and medium DHSR.
Seismic well tie is a critical process to verify the time-depth relationship of a well. This process requires density and sonic transit time data. However, sonic logs are usually not acquired due to cost saving, unfavorable well path, or other operational issues. Attempts to generate synthetic logs by Gardner equation, porosity correlation, or depth correlation did not provide the required accuracy. Therefore, the goal of our project was to generate synthetic sonic logs using machine learning technique for seismic well ties. This paper will compare the different methods tested, compare the results and lists the advantages of using Machine Learning.
This approach uses machine learning technique to create synthetic sonic logs. The machine learning model is trained to predict sonic log from other relevant logs. The model representativeness is confirmed by blind tests, which consists of two steps. The first step compares the synthetic sonic logs to the actual sonic logs. In the second step, four synthetic seismograms are generated from actual sonic, machine learning synthetic sonic, Gardner predicted sonic, and averaged constant sonic. The seismic well ties are compared between those four synthetic seismograms. Once the machine learning synthetic and actual logs show similar results, the model is deemed good and can be applied on wells that do not have sonic logs. The synthetic seismograms are then generated using synthetic sonic logs for all the wells that do not have actual sonic logs.
The use of synthetic sonic logs gives us the ability to Generate synthetic seismogram to tie wells that do not have sonic data Reduce the number sonic data acquisition, saving time and money Reduce the risk of long logging string getting stuck in the hole that would requires fishing operations and its associated cost.
Generate synthetic seismogram to tie wells that do not have sonic data
Reduce the number sonic data acquisition, saving time and money
Reduce the risk of long logging string getting stuck in the hole that would requires fishing operations and its associated cost.
Diagnostic tools such as microseismic, microdeformation, and fiber optics have been successfully used in unconventional basins for many years to identify characteristics of hydraulic fractures (Warpinski et al. 2014). More recently there has been a push for integrating multiple diagnostics for better understanding of fracture characteristics and development for overall well planning and increasing ultimate recovery. Well spacing is crucial for proper development of each asset within every resource play in North America. The datasets used for this study will demonstrate how a more effective development of an asset based on well spacing can be created using integrated fracture diagnostic analysis to understand proppant distribution.
The Marcellus formation has begun to attract more attention from the oil and gas industry. Despite being the largest shale formation and biggest source of natural gas in the United States, it has been the subject of little research. To fill this gap, this study experimentally examined the rock properties of twenty core samples from the formation.
Five tests were performed on the core samples: X-ray computerized tomography (CT) scan, porosity, permeability, ultrasonic velocity, and X-ray diffraction (XRD). CT-scans were performed to identify the presence of any existing fracture(s). Additionally, helium was injected into the core samples at four different pressures (100 psi, 200 psi, 300 psi, and 400 psi) to determine the optimal pressure for porosity measurements. Complex Transient Method was employed to measure the permeabilities of the core samples. Ultrasonic velocity tests were conducted to calculate the dynamic Young's moduli (E) and the Poisson's ratios (ν) of the core samples at various confining pressures (in increments of 750 psi between 750 psi and 4,240 psi). Finally, the mineralogical compositions of the core samples were determined using the XRD test.
The results of the CT-scan experiments revealed that seven core samples contained fractures. The porosity tests yielded an optimal pressure of 200 psi for porosity measurement. The measured porosities of the samples were between 6.43% and 13.85%. The permeabilities of the samples were between 5 nD and 153 nD. The results of the ultrasonic velocity tests revealed that at the confining pressure of 750 psi, the compressional velocity (Vp) ranged from 18,411 ft/s to 19,128 ft/s and the average shear velocities (Vs1 and Vs2) ranged from 10,413 ft/s to 11,034 ft/s. At the same confining pressure, the Young's modulus and Poisson's ratio ranged from 9.8 to 10.8 million psi and 0.25 to 0.28, respectively. Increase in the confining pressure resulted in increases in the Vp, Vs, Young's moduli, and Poisson's ratios of the samples. The results of the XRD test revealed that the samples were composed of calcite, quartz, and dolomite.
This study is one of the first to characterize core samples obtained from the formation outcrop by performing five tests: CT-scan, porosity, permeability, ultrasonic velocity, and XRD. The results provide detailed insights to researchers working on the formation rock properties.
Zaluski, Wade (Schlumberger Canada LTD) | Andjelkovic, Dragan (Schlumberger Canada LTD) | Xu, Cindy (Schlumberger Canada LTD) | Rivero, Jose A. (Schlumberger Canada LTD) | Faskhoodi, Majid (Schlumberger Canada LTD) | Ali Lahmar, Hakima (Schlumberger Canada LTD) | Mukisa, Herman (Schlumberger Canada LTD) | Kadir, Hanatu (Schlumberger Canada Limited now with ExxonMobil) | Ibelegbu, Charles (Schlumberger Canada Limited) | Pearson, Warren (Pulse Oil Operating Corp) | Ameuri, Raouf (Schlumberger Canada Limited) | Sawchuk, William (Pulse Oil Operating Corp)
Enhanced oil recovery (EOR) is an economic way of producing the remaining oil out of previously produced Devonian Pinnacle Reefs in the Nisku Formation within the Bigoray area of Alberta. To maximize the recovery factor of the remaining oil, it was necessary to first characterize the geological structure, matrix reservoir properties, vugular porosity and the natural fracture network of these two carbonate reefs. This characterization model was then used for reservoir simulation history matching and production forecasting further discussed by (
Zhou, Xu (Louisiana State Unviersity) | Tyagi, Mayank (Louisiana State Unviersity) | Zhang, Guoyin (China University of Petroleum - Beijing) | Yu, Hao (Southwest Petroleum University) | Chen, Yangkang (Zhejiang University)
With recent developments in data acquisition and storage techniques, there exists huge amount of available data for data-driven decision making in the Oil & Gas industry. This study explores an application of using Big Data Analytics to establish the statistical relationships between seismic attribute values from a 3D seismic survey and petrophysical properties from well logs. Such relationships and models can be further used for the optimization of exploration and production operations.
3D seismic data can be used to extract various seismic attribute values at all locations within the seismic survey. Well logs provide accurate constraints on the petrophysical values along the wellbore. Big Data Analytics methods are utilized to establish the statistical relationships between seismic attributes and petrophysical data. Since seismic data are at the reservoir scale and are available at every sample cell of the seismic survey, these relationships can be used to estimate the petrophysical properties at all locations inside the seismic survey.
In this study, the Teapot dome 3D seismic survey is selected to extract seismic attribute values. A set of instantaneous seismic attributes, including curvature, instantaneous phase, and trace envelope, are extracted from the 3D seismic volume. Deep Learning Neural Network models are created to establish the relationships between the input seismic attribute values from the seismic survey and petrophysical properties from well logs. Results show that a Deep Learning Neural Network model with multi-hidden layers is capable of predicting porosity values using extracted seismic attribute values from 3D seismic volumes. Ultilization of a subset of seismic attributes improves the model performance in predicting porosity values from seismic data.