Hydraulic fracturing with slickwater is a common practice in developing unconventional resources in North America. The proppant placement in the fractures largely determines the productivity of the well as it affects the conductivity of fractures. Despite the wide use of the slickwater fracturing and the importance of the proppant placement, the proppant transport is still not fully understood and the efficiency of the proppant placement is mostly bound to the changes to proppant properties, friction reducers, and guar technology. Although the degradable fiber is currently used in some cases, it has not been well investigated. In this experimental study, we conducted proppant transport experiment using different fluid composition of fiber and guar gum in three types of proppant transport slot equipment. The results indicate that the use of degradable fibers with or without the guar gum as viscosifier can produce fracture slurry applicable in both conventional and unconventional fracturing helping proppant placement in the reservoir.
Zones of increased fracture density related to the tectonic disturbances and connected to the protrusions and recesses of the consolidated basement were identified with the application of seismo-dynamic analysis of the seismic data. This is done for the first time on Povkhovskoe oil field located in Western Siberia.
Daily and monthly rates of the producing wells in relation to their location within the geological structure were analyzed. The analysis showed a pattern of increased well productivity by more than 2 times when approaching the areas with high density of fractures. At a distance of more than 500 m from the tectonic disturbances the fluid inflow rates significantly decrease and the performance of hydraulic fracking provides only short-term effect. The deterioration of the reservoir properties is due to a decrease in the value of the reservoir rock permeability because of the decrease in the proportion of fractures and the predominance of the pore space. Reservoir type changes from fractured or fractured-porous reservoir type to porous-only type.
The dependence of high oil saturation of the productive formation from the presence of the tectonic disturbances was recorded. Exploitation of producing wells confirms the assumption of oil moving along the sub-vertical zones of destruction and contributing to the primary target Upper Jurrasic-1 reservoir. Drilling of sidetracks from low oil rate and high water saturation wells in the areas with increased fracture network identified by seismo-dynamic analysis showed a high efficiency of the operations leading to a high-rate production including a substantially lower water-cut oil production (up to 5% of water) at the site where the surrounding production wells have water-cut of 99-100%. Meanwhile, reservoir characteristics of the Upper Jurrasic-1 formation are identical. Based on the results of research identified were prospective deposits for the drilling of production wells on the edges of the hydrocarbon accumulation in areas with high fracture density and suggested were the borehole sidetracks of wells that are plugged and abandoned.
Thus, the detailed structural and tectonic structure of the basement surface and the Jurassic sediments allows to select complex, small-scale geological features, which will be very prospective for the detection of small oil deposits, to specify the location of exploration wells, to start the search for deposits in areas of sub-vertical degradation in the Middle and Lower Jurassic sediments, basement rocks in areas with overlying hydrocarbon deposits already in development. Identifying zones of high density fracturing, including those associated with horizontal shear zones, allows to adjust the contour outlines of the alleged existing deposits and to discover prospective areas with the increased permeability. Described zones and areas are likely to be located close to faults originating in the basement.
Krutko, Vladislav (Gazpromneft Scientific and Technical Center) | Belozerov, Boris (Gazpromneft Scientific and Technical Center) | Budennyy, Semyon (Moscow Institute of Physics and Technology, Center for Engineering and Technology) | Sadikhov, Emin (Moscow Institute of Physics and Technology, Center for Engineering and Technology) | Kuzmina, Olga (Moscow Institute of Physics and Technology, Center for Engineering and Technology) | Orlov, Denis (Skolkovo Institute of Science and Technology) | Muravleva, Ekaterina (Skolkovo Institute of Science and Technology) | Koroteev, Dmitri (Skolkovo Institute of Science and Technology)
A framework for porous media topology reconstruction from petrographic thin sections for clastic rocks is proposed. The framework is based on two sequential stages: segmentation of thin sections imagesinto grains, porous media, cement (with further mineralogical classification of segmented elements) and reconstructing a three-dimensional voxel model of rock at pore scale.
The framework exploits machine learning algorithms in order to segment2D thin section images, perform structural and mineralogical classification of grains, cement, pore space, and reconstruct 3D models of porous media. Segmentation of petrographic thin section images and mineral classification of the segmented objects are performed by the means of combination of image processing methods and Convolutional Neural Networks (CNNs). The 3D porous media reconstruction is done by means of the Generative Adversarial Networks (GANs) are applied to the segmented and classified 2D images of thin sections.
As the criteria of the reconstruction quality, the following metrics were numerically calculated and compared for original and reconstructed synthetic 3D models of porous rocks: Minkowski functionals (porosity, surface area, mean breadth, Euler characteristic) and absolute permeability. Absolute permeability was calculated using pore network model. The 3D reconstruction framework was tested on a set of thin sections and CT tomograms of the clastic samples from the Achimovskiy formation (Western Siberia). The results showed the validity of the goodness-of-fit metrics based on Minkowski functionals for reconstruction the topology of porous media. The combined usage of CNN and GAN allowed to create a robust 3D topology reconstruction framework. The calculated poroperm characteristics are correlated with laboratory measurements of porosity and permeability.
The developed algorithms of automatic feature extraction from petrographic thin sections and 3D reconstruction based on these features allow to achieve the following goals. First is the reduction of the amount of the routine work done by an expert during petrographic analysis. Second leads to the reduction of the number of expensive and time-consuming CT scannings required for each physical sample in order to perform further absolute and relative permeability calculations. The proposed method can bring the petrographic thin section and CT data analysis to a new level and significantly change traditional core experiments workflow in terms of speed, data integration and rock sample preparation.
As data computing and big data driven analytics become more prevalent in a number of spatial industries, there is increasing need to quantify and communicate uncertainty with those data and resulting spatial analytical products. This has direct implication in oil & gas exploration and development where big data and data analytics continue to expand uses and applications of spatial and spatio-temporal data in the industry without providing for effective communication of spatial uncertainty. The result is that communications and inferences made using spatial data visuals lack crucial information about uncertainty and thus present a barrier to accurate and efficient decision making. With increasing cost awareness in oil & gas exploration and development, there is urgent need for methods and tools that help to objectively define and integrate uncertainty into business decisions.
To address this need, the Variable Grid Method (VGM) has been developed for simultaneous communication of both spatial patterns and trends and the uncertainty associated with data or their analyses. The VGM utilizes varying grid cell sizes to visually communicate and constrain the uncertainty, creating an integrated layer that can be used to visualize uncertainty associated with spatial, spatio-temporal data or data-driven products.
In this paper, we detail the VGM approach and demonstrate the utility of the VGM to intuitively quantify and provide cost-effective information about the relationship between uncertainty and spatial data. This allows trends of interest to be objectively investigated and target uncertainty criteria defined to drive optimal investment in improved subsurface definition. Examples are presented to show how the VGM can thus be used for efficient decision making in multiple applications including geological risk evaluation, as well as to optimize data acquisition in exploration and development.
Today, uncertainty, if it is provided at all, is generally communicated using multiple independent visuals, aggregated in final displays, or omitted altogether. The VGM provides a robust method for quantifying and representing uncertainty in spatial data analyses, offering key information about the analysis, but also associated risks, both of which are vital for making prudent business decisions in oil & gas exploration and development.
Field presented here is located in offshore Abu Dhabi, consisting of multi-stacked reservoirs with different fluid and reservoir properties. In this paper, field development plan of one of reservoir has been presented which was initially planned to be developed with pattern water injection by more than 50 horizontal wells penetrating all the ten oil bearing layers from 9 well head towers. Reservoir consists of under-saturated oil with low gas-oil ratio and low bubble point. Initial 2 years of production was considered as Early Production Scheme (EPS period), during which significant amount of early production data consisting of downhole pressure measurement, time-lapse MDT, vertical interference data, PLT have been collected. Based on EPS data simulation model has been updated. Simulation fits well with the observed pressure gauge and time-lapse MDT data. Updated model gives good prediction for a year of blind test data (including saturation, MDT and porosity) collected from different wells several kilometers away from current development area reflecting a high level of confidence in areal and vertical connectivity representation. Considering other reservoir uncertainties different Development plans have been screened using updated model in order to improve recovery factor and economics. Based on development plan screening study, optimized development option has been chosen for Full Field Development.
The Bone Spring and Wolfcamp formations of the Delaware Basin consist of mixed sediment gravity flow and suspension sedimentation deposits. These deposits exhibit high levels of heterogeneity both at and below core and log scales. A comprehensive approach integrating core and sub-core (nanoscale) data from two key wells and well logs within central Ward County was used to characterize small scale changes in lithology, rock properties, and reservoir quality. With this approach, a total of nine facies were identified; three siliceous mudstones [1, 2, 3], three siltstones [4, 5, 6], and three carbonates [7, 8, 9]. Each is comprised of different grain size distributions, textures, mineralogies, and pore types. Facies are not unique to an individual facies associations and cannot be predicted laterally in this study. Core-based measurements of source and reservoir properties were used along with qualitative observations from thin sections and high-resolution SEM images to identify facies as primary reservoir facies, secondary reservoir facies, and non-reservoir facies. Properties concerning source, reservoir, and mechanical quality were evaluated with respect to each facies and within each stratigraphic unit; 3rd Bone Spring, Wolfcamp A, Wolfcamp B, and Wolfcamp C.
Within the study area, 210 sq. miles in central Ward County along the eastern flank of the Delaware Basin, the Bone Spring and Wolfcamp formations are in the early mature oil window (0.69% – 0.88%Ro) and consist of an intercalation of siliceous mudstones [1, 2, 3], siltstones [4, 5, 6], and carbonates [7, 8, 9]. The four reservoir facies [1, 2, 4, 5] identified are organic rich with average wt.% total organic carbon (TOC) as follows; argillaceous siliceous mudstone  (3.1 wt.%, n=21), calcareous siliceous mudstone  (3.0 wt.%, n=15), argillaceous siliceous siltstone  (2.0 wt.%, n=7), and calcareous siliceous siltstone  (2.3 wt.%, n=7). Primary reservoir facies [1, 2] are richer in type II kerogen than the mineralogically comparable but coarser-grained secondary reservoir facies [4, 5], which contain more detrital grains and type III kerogen. Lower organic content in secondary reservoir facies [4, 5] is related to the dilution of organic matter via an extrabasinal influx of detrital grains and possible consumption by benthic fauna in oxygenated conditions. Degree of anoxia, bioturbation, and silica origin all have significant implications to reservoir quality as seen in the mineralogically similar non-reservoir biogenic siliceous mudstone facies  and the primary reservoir argillaceous siliceous mudstone facies . The former contains the least amount of detrital silica and organic matter of all facies observed. Early diagenesis of radiolaria and siliceous spicules source the microcrystalline authigenic quartz that was observed to occlude pore space in this non-reservoir facies . Despite the poor source potential and reservoir quality of this facies , the high amounts of microcrystalline authigenic quartz are beneficial to reservoir geomechanics. Implications to reservoir quality identified in this work have limited utility outside of the study area away from the flank of the basin, where bioturbation, degree of anoxia, and prevalence of extrabasinal facies differ. GRI saturations, MICP measurements, NMR (T2LM) data, and core-based TOC measurements indicate siliceous calcareous siltstone  as a facies potentially making up water-bearing carrier beds. Carbonate-rich facies [6, 7, 8, 9] were sampled least from core and more work must be done to better evaluate reservoir potential of these facies.
Core-based measurements of composition and reservoir quality indicate that porosity and permeability trend positively with clay, pyrite, and TOC, and negatively with carbonate. This relationship with porosity is most evident and statistically significant in the fine-grained facies [1, 2, 3, 4, 5], where silica is always the primary constituent. Relatively high clay content, upwards of 34 wt. %, in this study is not observed to negatively impact mechanical behavior. Porosity and TOC are highest in the Wolfcamp A and lowest in the lower Wolfcamp B subdivision, a trend observed beyond core control within the two key wells and on logs throughout the study area. This is largely a function of facies distribution. Based on stratigraphic architecture, facies distribution, and lack of thick non-reservoir carbonate barriers, the Wolfcamp A and upper Wolfcamp B may be considered one flow unit. This may allow well spacing and number of wells to be strategically optimized per drilling unit. Development strategies with respect to well spacing and well planning, may be better constrained with an understanding of each facies’ source potential, reservoir and mechanical quality, and distribution within each stratigraphic interval. Findings and interpretations from this research contribute to larger scale efforts being made to: 1) understand the role of diagenesis in unconventional reservoir quality; 2) recognize implications of depositional processes in unconventional reservoirs; and 3) image unconventional facies at the nano, micro, and macro scales.
The East Duvernay shale basin is the newest addition to the list of prolific reservoirs in Western Canada. Over the last 3 years, horizontal drilling and multistage hydraulic fracturing have increased significantly. Because much of the play is still relatively new, much of the drilling has been limited to single wells or two wells per pad. Due to the low permeability of the matrix, hydraulic fracturing is required to unlock the full potential of the East Duvernay field. Because geomechanics is a critical factor in determining the effectiveness of hydraulic fracture propagation, we examined how varying the pore pressure profiles affects modeled in situ stresses, hydraulic fracture geometries, and overall field optimization.
The pore pressure varies across the East Duvernay shale basin with the depth of the reservoir and other geomechanical parameters. The stresses in the Ireton, Upper Duvernay, Lower Duvernay, and Cooking Lake reservoirs also varies from the West to the East shale basins. High-tier logging, core measurements, and field data were used to build a mechanical earth model, which is then input for hydraulic fracture simulations. Whole core images and image logs indicate the Duvernay to be a naturally fractured reservoir. Because pore pressure is a direct input into the interpretation for in situ stresses, we sensitized on seven pore pressure profiles through the Ireton, Upper and Lower Duvernay, and Cooking Lake reservoirs. Typical pumping design currently being implemented in the Upper Duvernay was used to determine hydraulic fracture geometry based on the various in situ stress profiles. Black oil PVT models were built to run numerical reservoir simulation production forecasts to understand the effect of variations in geomechanical properties on well production performance. The effect of the varying hydraulic fracture properties on well spacing was also investigated for the seven pore pressure profiles, by combining the complex hydraulic fracturing and reservoir simulation.
The results clearly indicated the need to better understand, quantify, and constrain the in situ stress profiles variations with changes in pore pressure models. Hydraulic fracture length is greater within the Upper Duvernay when a constant pore pressure is modeled in the Ireton, Duvernay and the Cooking Lake, which leads to an overestimation of production. If a normal pore pressure is modeled in the Ireton with overpressure in the Duvernay, the hydraulic fracture grows into the Ireton and gives a more realistic production forecast. When the modeled pore pressure is gradually ramped up from the Lower Ireton into the Duvernay, slightly greater fracture length is created in the Duvernay but not enough to make a huge difference in forecasted production. These varying results for the modeled hydraulic fracture geometries impact the optimum number of wells per section.
As more wells come on production and the economic viability of the play is proven, operators will drill more wells per section. Thoroughly understanding the variations in geomechanics across the formations above and below the Duvernay is important. This objective of this study was to drive the conversation about the data that need to be collected and tests that should be run to support the optimization of economic development of the play for years to come.
Siddiqui, Fahd (University of Houston) | Rezaei, Ali (University of Houston) | Dindoruk, Birol (University of Houston / Shell International Exploration and Production) | Soliman, Mohamed Y. (University of Houston)
Prior knowledge of reservoir fluid type and properties aids in selecting and optimizing completion and surface facilities. Fluid properties prediction has an impact on in-place volumes and reservoir performance management including optimized well placement. We present a data-driven fluid variation modeling approach using machine learning. The aim is to predict the fluid type and oil API gravity for a given location and depth and optimize the completion design for the Eagle Ford shale.
Data from 9400 Eagle Ford shale wells were compiled, cleaned, and analyzed. Data was then divided into training and test sets. The test set was set aside for validation to prevent any training bias. Data visualization and statistical analysis was carried out, which revealed patterns and features within the training data. Three separate artificial neural networks (ANNs) were then constructed on those features, and a supervised learning algorithm was employed to train on the training set.
The first ANN predicts the oil API gravity based on a given coordinate: latitude, longitude and depth information. This network uses Mean Squared Error (MSE) loss function with the Root Mean Squared (RMS) regression optimizer. ANN-1 reported an error of 2.4 API which is well within process dependency of the API measurements and within the potential experimental errors. The second ANN predicts the most likely fluid type along with the probability, which can be used as a measure of confidence. ANN-2 uses the categorical cross-entropy loss function with the Adam optimizer (Kingma (2014)). Finally, ANN-3 predicts the hydrocarbon production of the first 12 months based on the well location, lateral length, depth, number of stages, proppant volume and gel volume. All three models were then validated on the test set, and a good match was obtained. Based on the data-driven models, an optimization scheme was created to maximize cash flow from the first 12 months of production based on varying the lateral length, the number of stages, proppant volume, and gel volume used. The resulting optimum parameters are then represented visually on the map of Eagle Ford, along with oil and gas production, and cash flow.
Even though the presented method was trained for Eagle Ford, data from other formations can be incorporated and re-trained, including other proxies for every additional basin, to create a general neural network predictive model on all formations; or to create smaller networks that would make accurate predictions within the specified formation. This approach will lead to a continuously improving and learning process for each additional field and play.