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Abstract The Bakken formation is well known for producing brine very high in total dissolved solids (TDS). Halite, calcium carbonate, and barium sulfate scales all can pose substantial production challenges. Trademarks of Bakken produced brine include elevated concentrations of sodium (>90,000 mg/L), chloride (>200,000 mg/L), and calcium (>30,000 mg/L), contrasted against low concentration of bicarbonate (50-500 mg/L). In the past 3 years, operators have experienced unexpected instances of severe calcium carbonate scale on surface where produced fluids from the production tubing commingled with the gas produced up the casing. Initially treated as one-off scale deposits despite the application of scale inhibitor, acid remediation jobs or surface line replacement were typical solutions. As time has passed, this issue has become more and more prevalent across the Bakken. Investigation of this surface issue discovered a most unexpected culprit: a low TDS, high alkalinity brine (up to 92,000 mg/L alkalinity measured to date) produced up the casing with the gas. When mixing with the high calcium brine typically produced in the Bakken, the resulting incompatibility posed remarkable scale control challenges. The uniqueness of this challenge required thorough analytical work to confirm the species and concentrations of the dissolved ions in the brine produced with the gas. Scale control products were tested to evaluate their abilities and limitations regarding adequate control of this massive incompatibility. The theory that corrosion contributed to this situation has been supported by a unique modelling approach. Once corrosion was identified as the likely source of the high alkalinity brine, corrosion programs were instituted to help address the surface scaling. This paper highlights the evaluations conducted to fully grasp the severity of the incompatibility, the theories put forth to date, work conducted to try to replicate the phenomena in the lab and in models, and chemical programs used in the field to address corrosion and scale. While not known to exist in other oilfield basins, conventional or unconventional, this discovery may have implications for the broader industry if similar situations occur. The possible explanations for why this may be happening may have implications for scale control, asset integrity, and potentially even the methods by which wells are produced.
Zhao, Xurong (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum at Beijing, China) | Liang, Tianbo (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum at Beijing, China) | Zan, Jingge (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum at Beijing, China) | Zhang, Mengchuan (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum at Beijing, China) | Zhou, Fujian (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum at Beijing, China) | Liu, Xiongfei (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum at Beijing, China)
Abstract Replacing oil from small pores of tight oil-wet rocks relies on altering the rock wettability with the injected fracturing fluid. Among different types of wettability-alteration surfactants, the liquid nanofluid has less adsorption loss during transport in the porous media, and can efficiently alter the rock wettability; meanwhile, it can also maintain a certain oil-water interfacial tension driving the water imbibition. In the previous study, the main properties of a Nonionic nanofluid-diluted microemulsion (DME) were evaluated, and the dispersion coefficient and adsorption rate of DME in tight rock under different conditions were quantified. In this study, to more intuitively show the change of wettability of DME to oil-wet rocks in the process of core flooding experiments and the changes of the water invasion front, CT is used to carry out on-line core flooding experiments, scan and calculate the water saturation in time, and compare it with the pressure drop in this process. Besides, the heterogeneity of rock samples is quantified in this paper. The results show that when the DME is used as the fracturing fluid additive, fingering of the water phase is observed at the beginning of the invasion; compared with brine, the fracturing fluid with DME has deeper invasion depth at the same time; the water invasion front gradually becomes uniform when the DME alters the rock wettability and triggers the imbibition; for tight rocks, DME can enter deeper pores and replace more oil because of its dominance. Finally, the selected nanofluids of DME were tested in two horizontal wells in the field, and their flowback fluids were collected and analyzed. The results show that the average droplet size of the flowback fluids in the wells using DME decreases with production time, and the altered wetting ability gradually returns to the level of the injected fracturing fluid. It can be confirmed that DME can migrate within the tight rock, make the rock surface more water-wet and enhance the imbibition capacity of the fracturing fluid, to reduce the reservoir pressure decline rate and increase production.
Lazutkin, Dmitry Mikhailovich (LLC Gazpromneft – Technology Partnerships) | Bukov, Oleg Vladimirovich (LLC Gazpromneft – Technology Partnerships) | Kashapov, Denis Vagizovich (Federal State Budgetary Educational Institution of Higher Education Ufa State Petroleum Technological University USPTU) | Drobot, Albina Viktorovna (GeoSplit LLC) | Stepanova, Maria Alexandrovna (GeoSplit LLC) | Saprykina, Ksenia Mikhailovna (GeoSplit LLC)
Abstract New geological structures – displaced blocks of salt diapirs’ overburden – were identified in the axial part of the Dnieper-Donets basin (DDB) beside one of the largest salt domes due to modern high-precision gravity and magnetic surveys and their joint 3D inversion with seismic and well log data. Superposition of gravity lineaments and wells penetrating Middle and Lower Carboniferous below Permian and Upper Carboniferous sediments in proximity to salt allowed to propose halokinetic model salt overburden displacement, assuming Upper Carboniferous reactivation. Analogy with rafts and carapaces of the Gulf of Mexico is considered in terms of magnitude of salt-induced deformations. Density of Carboniferous rocks within the displaced flaps evidence a high probability of hydrocarbon saturation. Possible traps include uplifted parts of the overturned flaps, abutting Upper Carboniferous reservoirs, and underlying Carboniferous sequence. Play elements are analyzed using analogues from the Dnieper-Donets basin and the Gulf of Mexico. Hydrocarbon reserves of the overturned flaps within the study area are estimated to exceed Q50 (Р50) = 150 million cubic meters of oil equivalent.
Abstract This study explains how production performance of the multi-fractured horizontal wells can be divided into two key contributing components: (1) geographical location and (2) completion strategy. Furthermore, we show how to quantify the contribution of these two independent components to production, and to understand the variations in key performance drivers across the evaluated field. Being able to differentiate these contributions allows us to compare well performance in a consistent manner and identify potential upside opportunities such as re-frac candidates, infill well development, and operator benchmarking. Further analysis uses multiple benchmarks to evaluate operator performance and assess how underperforming operators can optimize their completion strategies. We use a novel machine learning approach – a combination of XGBoost and Factor Contribution Analysis (FCA) - that not only allows for field-wide well evaluations, but also provides a quantifiable contribution of each feature to production. Our approach generates a production prediction model and takes into account the completion parameters and geological information for each well. The final model can be used to either predict future performance of a field/well, or to understand reservoir and completion characteristics. This study focuses on the latter and provides an approach to understand the main influencing factors behind well performance as a result of location and completion strategies. Our study is conducted on three major unconventional plays, Haynesville, Eagle Ford and Bakken, where we demonstrate how different completion features (e.g., lateral length, proppant volume, fluid volume) affect production data, and what we could expect in terms of production should the well have been completed differently. We show how to combine the effect of individual controlling factors (e.g., location, depth, lateral length, proppant volume, fluid volume and well spacing) to appropriately characterize the performance of each well in terms of two key components, location and completion. This enables us to quantify what portion of the production is a result of rock quality and how much is due to its completion strategy. This technique also allows us to quantify and relate each of these features, and highlight areas with desirable geological features, as well as good candidates for re-frac jobs. Moreover, we benchmark different operators’ performance as it relates to changing rock quality and completion strategies. The proposed procedure allows us to answer a series of important questions that are asked quite often. These include questions such as, is a well's production performance a factor of its location or the way it was completed? How to quantify, separately, the contribution of completion and location to production? Can sweet spots be identified in an area using production data? Does completion effectiveness vary with location, or operator, or year?
Abstract To maximize the coal seam gas production, it is critical to use geosteering to maintain the drill bit within coal seams. The gamma ray log is usually used as the coal/noncoal indicator to maintain the drill bit; however, the gamma ray log is a lagging indicator because its sensors are behind the drill bit. This can impair the drilling efficiency and subsequently increase non-productive time (NPT). In this paper, a machine learning approach is implemented to generate the gamma ray log (regression task) and identify coals (classification task) during drilling. The data is first filtered with positive rate of penetration (ROP) and depth increment. Then outliers are removed and samples are classified as coal/noncoal using the gamma ray log. The machine learning algorithm (i.e., XGBoost) is implemented to train and test the samples. To evaluate the results, the R, mean absolute error (MAE), and root mean square error (RMSE) are used for the regression task. The precision, recall, and F1 score are used for the classification task. A case study is performed with data from one well in the Surat Basin, Australia. It is observed that ROP is usually higher in coals and lower in noncoal formations. The R of the regression task from XGBoost is 0.6175. The MAE and RMSE are 1.293 counts per second (CPS) and 1.996 CPS, respectively. The general trend of generated gamma ray log is close to the original gamma ray log from logging-while-drilling (LWD) tools. For the coal/noncoal classification task, the precision, recall, and F1 score are 0.85, 0.88, and 0.86, respectively. Thus, XGBoost can effectively distinguish coals from noncoal formations during in-seam drilling. The developed machine learning model has the potential to identify coals and improve drilling efficiency during real-time in-seam drilling.
Abstract This study used production data and a novel machine learning approach utilizing Factor Contribution Analysis (FCA) to highlight geologic sweet spots for multiple US on-shore basins. Each model result was validated against key geologic parameters to establish if the geologic conditions exist for the modeled sweet spots. Further analysis shows how geologic production drivers can change across each play. Geologic assessments rely primarily on parameters related to tectonic/depositional settings, reservoir storage, saturations, hydrocarbon phase, and wellbore deliverability to define resource play outlines. These same parameters are often used to identify geologic sweet spots and help explain production drivers. Available data resolution varies widely across plays depending on maturity of the play and/or complexity of subsurface relationships. Using only publicly available production and well completion data, XGBoost and SHAP machine learning approaches were used to identify play sweet spots and prepare reservoir quality maps. The focus of this study was on validating the results obtained from machine learning of production variables by using geological information. These geological data were derived from multiple sources including regional interpretations and incorporating geologic parameter cutoffs traditionally used for highlighting geologically favorable areas. Regional play data was provided through public data sources, technical publications, and investor presentations. Parameter cutoffs were overlayed with model results to validate the process. The machine learning methodology utilizing FCA was used to highlight production sweet spots across multiple US on-shore basins. This study has validated the production-based machine learning results through geologic analysis. The result was a strong correlation between key geologic parameters and model results. Specific relationships are established between the geology and model results that allow for deeper insights to be uncovered regarding changing geologic production drivers across the play. This analysis has corroborated independently that machine learning of production variables does result in a reliable characterization of reservoir rock quality. This type of analysis has been applied successfully to several unconventional resource plays, and provides significant impetus for intelligent use of explainable machine learning modeling. Moving forward, application of similar approaches can not only validate model results, but also highlight key geologic production drivers. Validation of the machine learning methodology allows users to better answer questions related to completion effectiveness, well evaluations, and development strategies.
Liu, Xinghui (Chevron Corporation) | Tan, Yunhui (Chevron Corporation) | Singh, Amit (Chevron Corporation) | Waddle, Robert (Chevron Corporation) | Hilarides, Kurt (Chevron Corporation) | Forand, David (Chevron Corporation) | Liang, Baosheng (Chevron Corporation) | Khan, Shahzad (Chevron Corporation) | Rijken, Margaretha (Chevron Corporation)
Abstract Economical development of unconventional resources continues demanding applications of new and innovative technologies. The Hydraulic Fracturing Test Site (HFTS-1) in the Midland Basin provides a unique dataset to further our understanding of fracture dynamics and its relationship with geomechanics. Cased hole DFIT data from three horizontal wells and open-hole micro-stress test data at four different depths from a pilot well were analyzed. Both horizontal minimum stress and reservoir pressure data obtained from these tests were used to calibrate the mechanical earth model derived from petrophysical interpretations. The calibrated mechanical earth model was used to build an unconventional fracture model (UFM) that captures both stress shadowing effects and interactions with natural fractures. Several challenges were encountered during the UFM modeling efforts. A discrete fracture network (DFN) model derived from image logs and core data was upscaled to reflect fracture complexity observed from microseismic data and adjusted to meet the UFM requirement. Stress anisotropy was another uncertainty and was estimated by comparing UFM simulation results with microseismic data. The calibrated fracture model was used to evaluate fracture treatments for over 400 stages in eleven child wells targeting two Wolfcamp formations. Another objective of this study was to assess reservoir depletion from a geomechanics perspective. Geomechanical modeling was carried out to assess the effects of depletion around two parent wells using actual production history. Geomechanical modeling results indicated that no significant stress rotation occurred due to depletion around the two parent wells after 15 months of production. Microseismic depletion delineation patterns were observed during the restimulation of the two parent wells. At the very initial stage of restimulation, many microseismic events occurred quickly, indicating the reservoir stress reached a critical state. This study demonstrated the values of fracture and geomechanical modeling and the importance of collaborations among multiple disciplines for unconventional development. This study presented an integrated earth-hydraulic fracture-geomechanical modeling workflow, and evaluated stimulation effectiveness and geomechanics impacts for unconventional development. Lessons learned from this study were shared in hope to provide values for future integrated modeling efforts and ultimately optimizing development strategy on well spacing and parent-child depletion effects.
Abstract CO2 is recognized as an effective EOR method in low permeability reservoir, but whether it is a feasible formation energy supplying method or not for the normal pressure tight oil reservoir is still unknown. Complex fractures are developed in Honghe Chang 8 tight sandstone reservoir and fractured horizontal wells were put into production, resulting in complicated flow and serious gas channeling since CO2 flooding pilot was conducted in 2015. It is difficult to describe the fracture connectivity among wells quantitatively, even if the previous study shown two-stage fractures blocking technique is an effective method to control the gas mobility. And also it is still necessary to carry out further research work of inter-well fracture connectivity description and CO2 flooding technology policy to achieve the highest enhanced oil recovery. In the paper, based on geological and geophysics results, HH156 well block geological model was built up, according to CO2 flooding pilot, setting up multiphase and multicomponent dual medium numerical simulation model. History matching showed geological model could not meet the requirement of gas response characteristics because the fracture connectivity among wells could not be described reasonably. Reservoir engineering method was established to describe the fracture connectivity, complicated fractures among I-P wells have been revised. Further by author previous research on two-stage gas mobility controlling technique optimization, CO2 flooding technology policy optimizing was conducted with a new generation numerical simulator, on which gas injection mode, gas rate and CO2 miscible degree were analyzed. Research shows that for the wells along the principal stress direction, gas breakthrough quickly, presenting large-scale fracture developing, connective porosity is decided to be less than 0.1% and permeability is higher than 2000mD. Perpendicular to principal stress direction, gas breakthrough slowly, presenting small to medium-scale fracture development, connective porosity is more than 0.2% and permeability is lower than 500mD. Numerical simulation indicated that periodic asynchronous gas injection is the best way for CO2 flooding. By effectively controlling gas mobility, with gas rate increasing, oil production improve gradually, the optimal gas rate is 24±t/d(0.011±HCPV/a). CO2 flooding is immiscible process, miscible pore volume amounts to 0.86%HCPV. After 10 years, the average reservoir pressure keeps 101% level of original pressure. The sweep efficiency was improved from 1.4% (previous CO2-foam flooding) to 24.1%, reciprocal of gas utilization ratio is 0.23t/t, oil recovery factor reaches to 6.08%. CO2 flooding is expected to be one of the important development modes for normal pressure tight oil reservoir. large-scale fractures should be avoided and small to medium-scale fractures are the favorable area for expanding CO2 sweep efficiency. Results are not only advisable for the ordos normal pressure tight reservoir development, but also for the similar tight reservoir in the world.
Abstract The technology of hydraulic fracturing in horizontal wells has been widely applied to successfully developing unconventional reservoirs. However, observations show that that there are occasions that lead to irregular performances resulting in poor recoveries. This paper examines one of the causes for such poor recovery performnces. We studied the nonuniformity of stimulated volume size among staged fractures and the impact of such nonuniformity on well production and the ultimate recovery. The inspiration comes from studying microseismic fracture mappings from several wells in three shale plays. These images point to the existence of nonuniform stimulated volumes. The maps of various microseismic events show that stimulated reservoir volumes (SRVs) vary from stage to stage because of rock lithology, treatment volumes or other factors. We conducted model studies to simulate the impact of fractures with different SRVs on horizontal well production. We observed the presence of crossflow using the log-log plot of inverse rate versus Time. The results of the simulation show that the presence of crossflow can have a negative effect on well production and recovery factors. We examined the nature of crossflow as a function of fracture conductivities and stimulated volumes. This paper for the first time shows the importance of prior detecting of unequal SRVs as seen on the microseismic data for potentially refracturing intervals with poor SRVs before a well is placed on production.
Silva, Cristhian F. Aranguren (Schulich School of Engineering-University of Calgary) | Gomes, Antonio Ch. S. (Schulich School of Engineering-University of Calgary) | Aguilera, Roberto (Schulich School of Engineering-University of Calgary)
Abstract The objective of this research is to examine the link between total petroleum systems (TPS), geomodeling and reserves determination with a view to improve production forecasting, and to increase petroleum rates and recoveries while keeping an eye on economics and externalities. Petroleum as used in this paper includes oil, dry gas, and natural gas liquids. The proposed method links geoscience and engineering through a multi-disciplinary team. Proper understanding of the petroleum system is the foundation for rigorous geomodeling and for increasing economically petroleum rates and recoveries. The idea is not to convert geoscience into engineering or vice versa. Rather the idea is to make sure that members of the team understand properly the data that the other participants need, and that they communicate each other precisely what they need and the form in which the data it must be supplied. The anticipated outcome is that the interaction will lead to a better understanding of the reservoir(s) and consequently improved forecasting of petroleum rates and recoveries. Results of the study indicate that engineering deals properly with three essential elements of TPS: reservoir, seal, and overburden rock. However, there is a lack of proper understanding of the first essential element: source rock. Similarly, engineering has a good handle on the fourth essential process of the petroleum system: the accumulation. But there is a lack of proper understanding of the first three processes: trap formation, generation, and migration of hydrocarbons. This paper looks at filling the gap in this lack of understanding. Having clear knowledge of the type of data needed by engineering from the beginning is important, for instance, when building variograms and performing geomodeling. For example, geoscience can generate 3D geological grids using hundreds or thousands of millions of cells. But engineering can only use in practice a fraction of those cells for simulating multiphase fluid flow. Thus, upscaling and downscaling of the geologic grid is necessary in some cases. The novelty of this paper is the linking of the TPS, geomodeling, forecasting and reserves determination of unconventional reservoirs. This type of linking leads geoscience and engineering to talk the same language with a view to improve communication. The result is better, faster, and more accurate studies that improve production forecasting, economic rates, and recoveries of petroleum reservoirs.