Oil production from shale and tight formations will increase to more than 6 million barrels per day (b/d) in the coming decade, making up most of total U.S. oil production (> 50%). However, achieving an accurate formation evaluation of shale faces many complex challenges. One of the complexities is the accurate estimation of shale properties from well logs, which is initially designed for conventional reservoirs. When we use the well logs to obtain shale properties, they often cause some deviations. Therefore, in this work, we combine cores and well logs together to provide a more accurate guideline for estimation of total organic carbon, which is primarily of interest to petroleum geochemists and geologists.
Our work is based on Archie's equation. Resistivity log will lead to some incorrect results, such as total resistivity, when we follow the conventional interpretation procedure in well logs. Porosity is another complex parameter, which cannot be determined only by well log, i.e. density, NMR, and Neutron log. Therefore, the flowchart of TOC calculation includes five main parts: (I) the shale content calculation using Gamma log; (II) the determination of shale distributions using Density and Neutron logs and cross-plot; (III) the calculation of total resistivity at different distribution types; (IV) obtaining porosity using core analysis, NMR and density logs; and (V) the calculation of TOC from modified Archie's equation.
The results indicate that the shale content has a strong effect on estimation of water saturation and hydrocarbon saturation. Especially, the effect of shale content is exacerbated at a low water saturation. A more accurate flowchart for TOC calculation is established. Based on Archie's equation, we modify total resistivity and porosity by combining Gamma Log, Density Log, Neutron Log, NMR Log, and Cross-plot. An easier way to estimate porosity is provided. We combine the matrix density and kerogen density together and obtain them from core analysis. Poupon's et al. (1954) laminar model has some limitations when applying in shale reservoirs, especially at a low porosity.
Literature surveys show few studies on the flowchart of TOC calculation in shale reservoirs. This paper provides some insights into challenges of well logs, core analysis in shale reservoirs and a more accurate guideline of TOC calculation in shale reservoirs.
This paper presents a description of the technology for numerical simulation of thermal gas treatment on Bazhenov formation, taking into account features of Bazhenov formation and thermal gas treatment and assumptions of the simulator.
First of all it is required to determine the following parameters: voidness (porosity), permeability, fracturing, free oil (initial oil saturation), TOC (Total Organic Carbon). And also it is important to establish dependence of the parameters on temperature and pressure. Then, the process of thermal gas treatment can be conditionally divided into several stages: Effective production of light oil from drainable (permeable) zones (miscible displacement in front of the combustion front) Involvement of zones of reservoir containing kerogen during to heat treatment (pyrolysis reaction) and liberation of light oil and gaseous hydrocarbons from "locked" zones of reservoir. Involvement of the initially non-drainable (impermeable) zones of reservoir, named matrix (doesn’t mean the same as in dual porosity/permeability system). Especially these zones are the greatest interest among reservoir engineers because it can contain huge reserves of hydrocarbons.
Effective production of light oil from drainable (permeable) zones (miscible displacement in front of the combustion front)
Involvement of zones of reservoir containing kerogen during to heat treatment (pyrolysis reaction) and liberation of light oil and gaseous hydrocarbons from "locked" zones of reservoir.
Involvement of the initially non-drainable (impermeable) zones of reservoir, named matrix (doesn’t mean the same as in dual porosity/permeability system). Especially these zones are the greatest interest among reservoir engineers because it can contain huge reserves of hydrocarbons.
As a result of the steps described above, a 2D model was created, a numerical realization of the key processes taking place during thermal gas treatment on Bazhenov formation was carried out. Further, the main zones characterizing the process were identified and a physical justification for the individual indicators was given. Calculations of variants involving the matrix in the drainage process were carried out.
The calculated technological effect over a 50-year period of thermal gas treatment on the model (involving the production from matrix) was about 50% of the additional oil production, relative to the thermal gas treatment variant without involvement of matrix.
According to the results of the work, an evaluation of the efficiency of wet combustion was carried out during thermal gas treatment. The results of the calculations clearly demonstrate the advantage of using wet combustion. It is as stimulation of production of reservoir oil, as of additional synthetic oil as a result of kerogen pyrolysis reaction.
Wang, Ningyu (Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, TX 78712, USA) | Prodanovic, Maša (Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, TX 78712, USA) | Daigle, Hugh (Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, TX 78712, USA)
Precipitation and deposition of paraffin wax and hydrates is a major concern for hydrocarbon transport in pipelines, tiebacks, and other production tubing in cold environments. Traditionally, chemical, mechanical, and thermal methods are used to mitigate the deposition at the expense of production interruption, complex maintenance, costs, and environmental hazards.
This paper studies the potential of nanopaint-aided electromagnetic pigging. This process has potentially low production impact, simple maintenance, low energy cost, and no chemical expense or hazards. The electromagnetic pig contains an induction coil that emits an alternating magnetic field. The alternating magnetic field induces heat in the nanopaint coating (i.e. coating with embedded paramagnetic nanoparticles) on the pipeline's inner wall and in the pipeline wall itself. The heat then melts and peels off the wax and hydrates adhering to the pipeline, allowing the hydrocarbon to carry them away.
We analyze the heating effectiveness and efficiency of electromagnetic pigging. The heating effectiveness is measured by the maximum pigging speed that allows deposit removal. The heating efficiency is measured by the ratio of the heat received by the wax over the total emitted electromagnetic energy, which we define as the pig induction factor.
Based on our numerical model, we compare the pig induction factor for different coil designs, different hydrocarbon flow rates, and different pig traveling speeds. We find that slower pig speed generally improves the pigging performance, that shorter solenoids with larger radius have higher efficiency, and that the oil flow does not considerably affect the process. We re-evaluate the maximum pig speed defined by the static pig model and confirm that a solenoid with larger radius allows higher pig speed.
We investigate the potential of a novel, low-maintenance electromagnetic pigging method that poses minimal interruption to production. This investigation is a basis for a new technology that stems from initial experimental investigation done by our collaborators. We here provide parameters for pig design and pigging protocol optimization, and will put them in practice in our future lab experiments.
Within a single field geophysical survey results always have a significant amount of data with a considerable variability and heterogeneity. This allows to classify geophysical data as a Big Data. Data scientists and software developers are increasingly recommending the use of machine learning techniques for data processing and interpretation. ML algorithms allow one to extract the most complete amount of useful information, reduce time costs, minimize the subjective factor in the decision-making process, etc. Early testing of these approaches began in the 60s, active practical implementation consisted in the 90s due to the large-scale implementation of seismic studies in 3D CDP modification 1. The emergence of new algorithms, modifications of the original data, the development of computational resources support the relevance of this topic at the present time. In seismic data interpretation machine learning approaches provide high performance in the process of automatic horizons picking, fault tracing, seismic facies analysis, sesimic inversion, reservoir prediction, etc. At the stage of seismic facies analysis application of the ML algorythms is especially effective since in the process of multiattribute classification the initial dataset increases severalfold in accordance with the number of calculated attributes 5-7, 9, 10.
Vorobev, Vladimir (Gazpromneft-GEO, LLC) | Safarov, Ildar (Gazpromneft-GEO, LLC) | Mostovoy, Pavel (Gazpromneft Science & Technology Centre, LLC) | Shakirzyanov, Lenar (Gazpromneft-GEO, LLC) | Fagereva, Veronika (Gazpromneft Science & Technology Centre, LLC)
Eastern Siberia is characterized by the extremely complex geological structure. The main factors include multiple faults, trappean and salt tectonics, the complex structure of the upper part of the section (0–1200 m) and its high-velocity characteristic (5000–6000 m/s), the high degree of rock transformation by secondary processes, low formation temperatures (10–30°C), the mixed fluid composition (gas, oil and water), and low net thicknesses (5–7 m) of productive layers. The fields of the region are among the most complex ones in the world according to the BP Company's statistics. New seismic and geologic model based on complex analyses of core, well logs, well tests, seismic and electromagnetic data allowed the Gazpromneft-GEO company to drill a series of successful wells.
Gazpromneft-GEO, LLC.holds three oil and gas exploration and production licenses within the Ignyalinsky, Vakunaisky and Tympuchikansky (Chona field) subsurface blocks (Russia, Eastern Siberia, Irkutsk Region and Republic of Sakha (Yakutia)). The area of the blocks is 6,855 sq.km, 3,050 sq.km of which are covered by the 3D seismic and high-density electric prospecting (
The work was carried out within the frames of scientific research and field works at the Gazpromneft-GEO, LLC. fields in Eastern Siberia. The high-density full-azimuth ground-based seismic using the UniQ technology was performed in Russia for the first time. The electric exploration with the near-field time-domain electromagnetic method was carried out along the same lines for the first time in Russia as well. This allowed to form the high-density cube of geoelectric properties. Model based on the wells (Facies model, Petrophysics model) and field geophysical data (3D seismic survey, 3D electric exploration, gravimetric survey, magnetic survey) complexation was made. The use of the approach allows to reduce the number of wells required for exploration of fields by 40%.
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.
We develop a novel ensemble model-maturation method that is based on the Randomized Maximum Likelihood (RML) technique and adjoint-based computation of objective function gradients. The new approach is especially relevant for rich data sets with time-lapse information content. The inversion method that solves the model-maturation problem takes advantage of the adjoint-based computation of objective function gradients for a very large number of model parameters at the cost of a forward and a backward (adjoint) simulation. The inversion algorithm calibrates model parameters to arbitrary types of production data including time-lapse reservoir-pressure traces by use of a weighted and regularized objective function. We have also developed a new and effective multigrid preconditioning protocol for accelerated iterative linear solutions of the adjoint-simulation step for models with multiple levels of local grid refinement. The protocol is based on a geometric multigrid (GMG) preconditioning technique. Within the model-maturation workflow, a machine-learning technique is applied to establish links between the mesh-based inversion results (e.g., permeability-multiplier fields) and geologic modeling parameters inside a static model (e.g., object dimensions, etc.). Our workflow integrates the learnings from inversion back into the static model, and thereby, ensures the geologic consistency of the static model while improving the quality of ensuing dynamic model in terms of honoring production and time-lapse data, and reducing forecast uncertainty. This use of machine learning to post-process the model-maturation outcome effectively converts the conventional continuous-parameter history-matching result into a discrete tomographic inversion result constrained to geological rules encoded in training images.
We demonstrate the practical utilization of the adjoint-based model-maturation method on a large time-lapse reservoir-pressure data set using an ensemble of full-field models from a reservoir case study. The model-maturation technique effectively identifies the permeability modification zones that are consistent with alternative geological interpretations and proposes updates to the static model. Upon these updates, the model not only agrees better with the time-lapse reservoir-pressure data but also better honors the tubing-head pressure as well as production logging data. We also provide computational performance indicators that demonstrate the accelerated convergence characteristics of the new iterative linear solver for adjoint equations.
Pavlov, Dmitry (Sakhalin Energy Investment Company Ltd.) | Fedorov, Nikolay (Sakhalin Energy Investment Company Ltd.) | Timofeeva, Olga (Sakhalin Energy Investment Company Ltd.) | Vasiliev, Anton (Sakhalin Energy Investment Company Ltd.)
This paper summarizes the results of 3 years collaborative efforts of the Geophysicist, Production Geologist and Reservoir Engineers from the Astokh Development Team and a Geochemist from the LNG plant laboratory on integration of reservoir surveillance and reservoir modelling.
In period 2015 – 2018 a large bulk of geological and field development data was collected in Astokh field, in particular: cased and open hole logs, core, open hole pressure measurements, flowing and closed-in bottom hole pressures, well test data, new 4D seismic surveys (2015, 2018), fluid samples. Since 2016, essential progress was made in oil fingerprinting for oil production allocation in Astokh field. Simultaneously, the need for update of static and dynamic models was matured upon gaining experience in dynamic model history matching to field operational data (rates, pressures, well intervention results). In other words, the need in update of geological architecture of the Astokh reservoir model was matured upon reaching critical mass of new data and experience. To revise well correlation, it was decided to combine different sorts of data, in particular seismic, well logs and core data and reservoir pressures. Different pressure regimes were identified for 3 layers within XXI reservoir. Pressure transient surveys were used for identification of geological boundaries where it's possible and this data was also incorporated into the model. Oil fingerprinting data was used for identification of different layers and compartments. Integration of pressure and oil geochemistry data allowed to identify inter-reservoir cross-flows caused by pressure differential. Based on all collected data, sedimentology model and reservoir correlation were updated based on sequential stratigraphy. As a result, a new static model of main Astokh reservoirs was built, incorporating clinoform architecture for layers XXI-1' and XXI-2. To check a new concept of geological architecture material balance model was used and matched to field data
Integration of geological and field operational data provided a key to more advanced reservoir management and development strategy optimization. Based on updated reservoir model, new potential drilling targets were identified. Also, with new well correlation, water flood optimization via management of voidage replacement ratio was proposed. The completed work suggests essential improvement in reservoir modelling process by inclusion of various well and reservoir surveillance data.
The paper consists of the following sections: Introduction Field geology Field development history Scope of work complete and main results Proposed well correlation update for XXI-1' and XXI-2 layers Integration of well logs, pressure and fluid analysis data Connectivity between layers XXI-S, XXI-1' and XXI-2 Integration of pressure and oil fingerprinting data Connectivity within layers XXI-S, XXI-1' and XXI-2 Results of pressure interference tests Testing of new well correlation concept in material balance model Proposed reservoir correlation updated based on seismic data New geological concept New depositional model Integration of core data Changes in reservoir architecture Conclusion Main results and impact on field development
Field development history
Scope of work complete and main results
Proposed well correlation update for XXI-1' and XXI-2 layers
Integration of well logs, pressure and fluid analysis data
Connectivity between layers XXI-S, XXI-1' and XXI-2
Integration of pressure and oil fingerprinting data
Connectivity within layers XXI-S, XXI-1' and XXI-2
Results of pressure interference tests
Testing of new well correlation concept in material balance model
Proposed reservoir correlation updated based on seismic data
New geological concept
New depositional model
Integration of core data
Changes in reservoir architecture
Main results and impact on field development
In this paper, the approach to multivariate static and dynamic modeling is considered on the example of an offshore field discovered in 2017. Based on the limited volume of information, the quantitative and qualitative description of uncertainties included further in the 3D modeling is made. This model is proposed to be used as a tool for prompt decision making when implementing a fast-track project with limited time between exploration and pre-FEED stages.
This seminar will teach participants how to identify, evaluate, and quantify risk and uncertainty in everyday oil and gas economic situations. It reviews the development of pragmatic tools, methods, and understandings for professionals that are applicable to companies of all sizes. The seminar also briefly reviews statistics, the relationship between risk and return, and hedging and future markets. Strategic thinking and planning are key elements in an organisation’s journey to maximise value to shareholders, customers, and employees. Through this workshop, attendees will go through the different processes involved in strategic planning including the elements of organisational SWOT, business scenario and options development, elaboration of strategic options and communication to stakeholders.