The identification of the fluid fill history is a necessity for the development strategy of any field, in particular in the Middle East where tectonic history is often reported to affect fluid distribution and contacts in many fields. The fluid fill concept for a low permeability carbonate field has been re-evaluated and modified from a tilted contact interpretation with imbibition of the deepest unit to a field-wide flat contact and primary drainage saturation distribution. The oil volumes in the reservoir under study are sensitive to minor changes in the structure and fluid fill due to the relatively low structural dip and low permeability transitional nature of the reservoir. The paper highlights the importance of removing preconceptions in data analysis and ensuring consistency on interpretations between different available data sources. It also demonstrates how data quality could completely change the fluid fill concept.
The three main reservoir units of the Lower Shuaiba A, Lower Shuaiba B and Kharaib have been charged from two oil migration events. Structural changes post the first primary drainage are revealed by regional seismic images of the shallower horizons. Due to the rock low permeability, the water saturations are above irreducible value and the whole interval is in the "transition zone". Kharaib unit was believed to be imbibed by the aquifer after charge and was not developed. Three possible fluid fill scenarios were investigated: a) tilted contact due to structural changes post-charge, b) imbibition of the deeper interval, c) primary drainage with field-wide flat contact related to the second pulse of charge. Each scenario impacts the development of the three units positively or negatively. Water saturation logs vs. True Vertical Depth plots were the main diagnostic tool used to rule out fluid fill scenarios. The plots were used to recognise lateral changes of the saturation profile and investigate imbibition signatures. Production data were also used to cross check the expected fluid fill scenario. The resistivity tools’ types and mud resistivities were examined.
Klie, Hector (DeepCast.ai) | Klie, Arturo (DeepCast.ai) | Rodriguez, Adolfo (OpenSim Technology) | Monteagudo, Jorge (OpenSim Technology) | Primera, Alejandro (Primera Resources) | Quesada, Maria (Primera Resources)
The Vaca Muerta formation in Argentina is emerging as one of the most promising resources of shale oil/gas plays in the world. At the current well drilling pace, challenges in streamlining data acquisition, production analysis and forecasting for executing timely and reliable reserves and resource estimations will be an overarching theme in the forthcoming years. In this work, we demonstrate that field operation decision cycles can be improved by establishing a workflow that automatically integrates the gathering of reservoir and production data with fast forecasting AI models.
We created a data platform that regularly extracts geological, drilling, completion and production data from multiple open data sources in Argentina. Data cleansing and consolidation are done via the integration of fast cross-platform database services and natural language processing algorithms. A set of AI algorithms adapted to best capture engineering judgment are employed for identifying multiple flow regimes and selecting the most suitable decline curve models to perform production forecasting and EUR estimation. Based on conceptual models generated from minimum available data, a coupled flow-geomechanics simulator is used to forecast production in other field areas where no well information is available. New data is assimilated as it becomes available improving the reliability of the fast forecasting algorithm.
In a matter of minutes, we are able to achieve high forecasting accuracy and reserves estimation in the Vaca Muerta formation for over eight hundred wells. This workflow can be executed on a regular basis or as soon as new data becomes available. A moderate number of high-fidelity simulations based on coupled flow and geomechanics allows for inferring production scenarios where there is an absence of data capturing space and time. With this approach, engineers and managers are able to quickly examine a feasible set of viable in-fill scenarios. The autonomous integration of data and proper combination of AI approaches with high-resolution physics-based models enable opportunities to reduce operational costs and improving production efficiencies.
The integration of physics-based simulations with AI as a cost/effective workflow on a business relevant shale formation such as Vaca Muerta seems to be lacking in current literature. With the proposed solution, engineers should be able to focus more on business strategy rather than on manually performing time-consuming data wrangling and modeling tasks.
Sanguinito, Sean (National Energy Technology Laboratory) | Cvetic, Patricia (National Energy Technology Laboratory) | Goodman, Angela (National Energy Technology Laboratory) | Kutchko, Barbara (National Energy Technology Laboratory) | Natesakhawat, Sittichai (National Energy Technology Laboratory)
It is becoming increasingly important to expand the fundamental understanding of geochemical interactions between CO2, fluids, and shale. These interactions will significantly impact the processes of 1) storing CO2 in hydraulically fractured shale formations, 2) using CO2 as a fracturing agent, and 3) enhancing hydrocarbon recovery in shales via CO2 flooding. In this work, we use in-situ Fourier Transform infrared spectroscopy (FT-IR), feature relocation scanning electron microscopy (SEM), and surface area and pore size analysis using volumetric gas sorption and density function theory (DFT) methods to characterize and quantify the reactions that occur between CO2, fluids, and shale. Several shale samples from across the U.S. were analyzed including the Marcellus, Utica, and Eagle Ford Shales. CO2 will be injected into shale formations where it will interact with shale surfaces (i.e. clays, organic matter), in-situ fluids (i.e. natural brines), and previously injected fracturing fluid. Currently, it is assumed that dry supercritical CO2 does not interact with or have any impact on reservoir rocks or seals. Our suite of measurements show CO2 interaction with clay and kerogen components of the shale, reactivity and etching of carbonate, and changes in pore sizes at the meso- and micro-scale. Very few studies are taking into account the reactivity of CO2 and fluids in the reservoir. The reactions that occur between CO2, fluids, and the shale may alter petrophysical properties such as porosity and permeability which may alter flow pathways potentially impacting the storage permeance of CO2 and the effectiveness of CO2 to behave as a fracturing agent or to mobilize hydrocarbons.
With increasing awareness and concern of CO2 emissions and climate change, there has been a shift in research efforts to evaluate the potential of shales to be used as CO2 storage reservoirs and effective natural seals for CO2 or hydrocarbons (Orr, F.M., 2009a.; Orr, F.M., 2009b; Romanov et al., 2015; Levine et al., 2016, Bacon et al., 2015). Current research is underway to determine the fundamental understanding of geochemical interactions between CO2, fluids, and shale. Fluids, such as formation fluids and fracturing fluids, can react with the CO2 and shale interface to alter formation properties (Jun, Y et al., 2013; Dieterich et al., 2016). This geochemical alteration of shale has been reported to directly affect porosity, permeability, flow paths, and integrity of the wellbore, seal, and formation (DePaolo and Cole, 2013). Additionally, the storage temperature and pressure conditions and the composition and chemistry of brine solution and hydraulic fracturing fluid have an impact on the geochemical alteration of the shale (specifically dissolution).
Zhang, Hui (PetroChina) | Wang, Lizhi (Schlumberger) | Wang, Zhimin (PetroChina) | Pan, Yuanwei (Schlumberger) | Wang, Haiying (PetroChina) | Qiu, Kaibin (Schlumberger) | Liu, Xinyu (PetroChina) | Yang, Pin (Schlumberger)
Located at the foothills of Tianshan mountains, western China, the Dibei tight gas reservoir has become one of the key exploration areas in last decade because of its large gas reserve potential. The previous exploration effort yielded mixed results with large variations of the production rates from these exploration wells and many rates are too low to be deemed as discovery wells. Petrophysical properties were excluded as controlling factors because these properties for most exploration wells are very similar. Under the large tectonic stress, heterogeneous natural fracture systems are induced and unevenly distributed in the reservoir, which might be the controlling factor for production. However, due to the limitation of the seismic data quality, quantitative fracture modeling with seismic is not possible for this field. A new method predicting the 3D occurrence of the natural fractures in the reservoir is needed.
In this study, geomechanics-based methods were used to predict the natural fracture systems in the reservoir. The methods started from classification of natural fracture systems based on borehole image and core data into either fold-related and/or fault-related fractures. Geomechanics-based structure restoration was conducted to compute the deformation and the perturbed stress field from the restoration of complex geological structures through time. A correlation was established between the fold-related perturbated stress field and the occurrence of fold-related fractures from wells to predict the 3D occurrence of this type of natural fractures. Meanwhile, the computation of the perturbed stress field around 3D discontinuities (i.e. faults) for one or more tectonic events was conducted by the Boundary Element Method (BEM) until a good match was achieved between the fault-related perturbed stresses and observed fault-related fractures from the wellbore. By using the output from the two methods, the discrete fracture network (DFN) model was constructed to explicitly represent the occurrence and geometry of the natural fracture system in the reservoir in a geological model. A geomechanical model was constructed based on an integrated workflow from 1D to 3D. The fracture stability was then calculated based on the 3D geomechnical model.
Detailed analysis was conducted among the DFN model, the geological model of the reservoir and productivity of the exploration wells, and very good correlation was revealed between the productivity of the exploration wells and the occurrence and geometry of the natural fractures and the structural position of the reservoir.
This study shows that geomechanics-based methods efficiently capture the occurrence of natural fracture systems and reveal the production-controlling factors of the tight gas reservoir. It demonstrates that geomechanics is a powerful tool to support successful exploration of the tight gas reservoir in tectonically stressed environments.
The Bowland Basin in Northern England contains a thick shale gas section (>5,000 ft) estimated to hold over 1300 TCF of total original gas in place of shale gas resource. In 2017, Cuadrilla Resources drilled a S-shaped pilot well, Preston New Road-1 (PNR-1), located in Lancashire, NW England. The plan was to drill, core, and log the Bowland Shale sequence with the primary objective to select the optimum landing depth for a subsequent side-tracked horizontal section (PNR-1z) of up to 3,280 ft length to be completed for multi-stage hydraulic fracturing. Another multi-stage horizontal well, PNR2, was also planned to be drilled afterward targeting a different stratigraphic horizon. Three vertical wells (PH-1, GH-1 and BS-1) were previously drilled in the Bowland Basin to a depth of 8,860-10,500 ft. Delays were encountered in the drilling of these wells due to multiple borehole stability problems. Specifically, in GH-1, the well required a side-track to reach the target depth. With the plan to drill four horizontal wells at Preston New Road, the first horizontal wells ever to be drilling in the Bowland shale, a rigorous geomechanical study was required to provide valuable insights for optimisation of the drilling programme.
A pre-drill geomechanical model was developed for the PNR-1 pilot well using advanced interpretation of available data and the gained experiences from the offset wells. A comprehensive pore pressure interpretation showed that Bowland shale is significantly over-pressured (0.69 psi/ft). The model was backed up by the observed splintery cuttings and gas shows in offset wells. It was concluded that this abnormal pore pressure combined with a tectonic strike-slip stress regime (with large horizontal stress anisotropy) and intrinsic anisotropic shale properties were the primary causative factors for drilling incidents. As a result of this study, the PNR-1 was successfully drilled and completed with minimal borehole stability problems despite the presence of narrow operating mud weight window in several stratigraphic intervals. The data acquisition program conducted included 114m of core from Upper and Lower Bowland shales, with the required logs for updating the geomechanical model. A comprehensive rock mechanics testing program was designed and conducted which resulted in better characterizing the anisotropic elastic properties and strength parameters of the Bowland Shale. This information was used to update the geomechanical model and aid the optimum landing decision depth of 2,180m for PNR-1z. A successful XLOT prior to drilling the 6" lateral section provided valuable data for further calibration of the stress model. The updated model was then used to develop safe operating mud weight window for PNR-1z, which helped drilling of the horizontal section to the TD at 11,233 ft MD (7,457 ft TVD) with no notable drilling problems.
This paper presents a summary of the geomechanical work performed for successful drilling and hydraulic fracturing operations in the Preston New Road exploration site and the outcomes and achievements.
Hao, Qian (Exploration and Development Research Institute & Science and Technology Department of Changqing Oilfield Company, CNPC) | Wang, Jiping (Exploration and Development Research Institute of Changqing Oilfield Company, CNPC) | Han, Dong (Science and Technology Department of Changqing Oilfield Company, CNPC) | Li, Wuke (Exploration & Development Research Institute of Changqing Oilfield Company, CNPC) | Liang, Changbao (South Sulige Operating Company of Changqing Oilfield Company, CNPC) | Dai, Libin (South Sulige Operating Company of Changqing Oilfield Company, CNPC) | Jia, Yonghui (South Sulige Operating Company of Changqing Oilfield Company, CNPC) | Qi, Congwei (TOTAL) | Zhai, Gaoqiang (TOTAL)
South Sulige operation project is an international cooperation development of tight sand gas field located in the Ordos Basin, Northwest China. The economy of the project relies on technical breakthrough to select good drilling location for getting higher Estimated Ultimate Recovery (EUR) rather than partners continually reducing annual investment and cost saving to survive in the global oil price fluctuations in the long run.
Although a total of 306 wells have been drilled and 1648 Km2 of 3D seismic data have been acquired and processed during the past 3 years, well drilling results were not as good as expected in terms of seismic sand thickness prediction and channel sand / shale discrimination. Seismic data quality indeed improved due to large efforts of the processing, even getting clear seismic images at reservoir level, however, at Upper Permian He8 Formation, the main gas producing target layer, seismic interpretation results are still difficulty to distinguish complicated fluvial depositions of this tight sand gas filed.
On the other hand, existing production data indicate that Absolute Open Flow (AOF) of the super good well which accounts for only 3% of the total drilled wells usually exceed 120×104m3/d, annual production of the super good well could exceed 2500 ×104m3, EUR of the super good well may exceed 2.4×108m3. Compared with the ordinary well, EUR of the super good well is 9.6 times that of the ordinary well. As a result, accurate predicting good drilling location and try to capture more super good wells remains the biggest challenge and the most attractive research direction for this international cooperation project.
Therefore, a different approach joint 3G (Geophysics, Geology, Gas Reservoir) integrated study is carried out by an international joint research team from Paris, France and Xi’an, China. This paper shows a new method of combining sedimentological model from wells results (static data include core description, typical channel E-logs parameters, semi-regional synthesis. dynamic data include AOF, annual production, EUR) with low value of Poisson's Ratio (PR) / amplitude maps which were defined in the study, aiming to identify areas where a given dominant fluvial facies could be predicted.
The paper's objective is to share the integrated study approach to get better understanding of such tight sand reservoir, and the proposed methodology opens new opportunities for predicting good drilling location, increase the probability of capturing more super good wells, lower the project development risk with best practices approach.
The bioclastic limestone reservoirs of Cretaceous period occupy an important position in the petroleum industry of Middle East. It is the carbonate heterogeneity that is challenging the accuracy of the reservoir prediction, which brings forward higher requirements for the seismic data quality. Besides, some seismic data are processed more than 10 years ago, the signal to noise ratio (SNR) is relative low due to the random noise and coherent noise like acquisition footprint anomalies. The acquisition footprint artifacts caused by acquisition and processing seriously suppress the true stratigraphic features, which can result in pitfalls in seismic interpretation, seismic attribute analysis as well as seismic inversion. While the pre-stack seismic data is usually unavailable, which means that the noise can hardly be subtracted by conventional pre-stack seismic processing workflows, such as statics, high-resolution velocity analysis and ground roll attenuation. Consequently, a comprehensive post-stack seismic data conditioning workflow is necessary to solve the above problems.
In order to improve the post-stack seismic data quality, a comprehensive data conditioning workflow are applied for noise suppression. Firstly, structural-oriented filtering is utilized to attenuate random noise and partial acquisition footprint artifacts. Then 2D waveform transform of seismic amplitude and filtered seismic attribute in x-y domain are calculated, to separate acquisition footprint anomalies (large wave number in kx-ky domain) from true structural signal (small wave number in kx-ky domain) by interactive analysis. The application of Laplace-Gaussian (LoG) filter deserves an obvious improvement in acquisition footprint suppression workflow. The comprehensive noise attenuation workflow in this paper can effectively remove both periodic and non-periodic noise to obtain higher signal to noise ratio (SNR) for post-stack seismic volume. In this way, the stratigraphic features (tidal-channel, reef-beach complex) can be more clearly depicted and some artifacts caused by noise will disappear in seismic attribute calculation, seismic inversion and reservoir prediction.
Lee, Wei Yi (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS) | Hamidi, Rosita (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS) | Ghosh, Deva (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS) | Musa, Mohd Hafiz (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS)
Noise is the unwanted energy in a seismic trace opposed to the signals corresponding to reflected energy from the subsurface features. Since it can overlap with the main signals' energy and conceal the geological information, noise attenuation is one of the most important steps in seismic data processing. The most common method is frequency filtering. However, due to its limitations on separating the noise from signals, this method usually results in hurting the signal. Hence, it is important to develop an alternative method that can attenuate the noise without affecting the signal. Filters based on time-frequency analysis of the data can have a better separation of the noise from signal as they maintain the time localization of events while presenting their frequency content simultaneously. One of the recent approaches to time-frequency analysis of signals is the Empirical Wavelet Transform (EWT) which provides adaptive wavelet filter bank for signal analysis. In this paper, a filter is designed based on EWT for random noise attenuation and is applied on both synthetic and real data.
The vast majority of grids for reservoir modeling and simulation workflows are based on pillar gridding or stairstep grid technologies. The grids are part of a feature-rich and well-established modeling workflow provided by many commercial software packages. Undesirable and significant simplifications to the gridding often arise when employing such approaches in structurally complex areas, and this will clearly lead to poor predictions from the downstream modeling.
In the classical gridding and modeling workflow, the grid is built in geological space from input horizon and fault interpretations, and the property modeling occurs in an approximated ‘depositional’ space generated from the geological space grid cells. The unstructured grids that we consider here are based on a very different workflow: a volume-based structural model is first constructed from the fault/horizon input data; a flattening (‘depositional’) mapping deforms the mesh of the structural model under mechanical and geometric constraints; the property modeling occurs in this depositional space on a regular cuboidal grid; after ‘cutting’ this grid by the geological discontinuities, the inverse depositional mapping recovers the final unstructured grid in geological space. A critical part of the depositional transformation is the improved preservation of geodetic distances and the layer-orthogonality of the grid cells.
The final grid is an accurate representation of the input structural model, and therefore the quality checking of the modeling workflow must be focused on the input data and structural model creation. We describe a variety of basic quality checking and structurally-focused tools that should be applied at this stage; these tools aim to ensure the accuracy of the depositional transformation, and consequently ensure both the quality of the generated grid and the consistent representation of the property models. A variety of quality assurance metrics applied to the depositional/geological grid geometries provide spatial measures of the ‘quality’ of the gridding and modeling workflow, and the ultimate validation of the structural quality of the input data.
Two case studies will be used to demonstrate this novel workflow for creating high-quality unstructured grids in structurally complex areas. The improved quality is validated by monitoring downstream impacts on property prediction and reservoir simulation; these improved prediction scenarios are a more accurate basis for history matching approaches.