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.
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.
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.
Africa (Sub-Sahara) Eni started production from the West Hub development project's Mpungi field in Block 15/06 offshore Angola. The startup follows the project's first oil from the Sangos field in November 2014 and the Cinguvu field last April. Mpungi will ramp up West Hub oil production to 100,000 B/D in the first quarter from a previous level of 60,000 B/D. The project also includes the future development of the Mpungi North, Ochigufu, and Vandumbu fields. Eni is the block operator with a 36.84% stake. Sonangol (36.84%) and SSI Fifteen (26.32%) hold the other stakes.
Decisions in E&P ventures are affected by Bias, Blindness, and Illusions (BBI) which permeate our analyses, interpretations and decisions. This one-day course examines the influence of these cognitive pitfalls and presents techniques that can be used to mitigate their impact. Bias refers to errors in thinking whereby interpretations and judgments are drawn in an illogical fashion. Blindness is the condition where we fail to see an unexpected event in plain sight. Illusions refer to misleading beliefs based on a false impression of reality.
In this study, conceptual numerical simulation models, with geomechanical properties incorporated, were employed to assess whether polymer flooding or a surfactant EOR process could be viable; with minimal damage to permafrost. These simulations considered the geological subdivisions of permafrost distribution in the subsurface which included: an active layer (seasonally frozen ground); taliks (unfrozen ground between the base of the active layer and permafrost layer and within the permafrost layer); and the unfrozen layer below the permafrost zone. In addition, a major oil zone was included in the model underlying the permafrost section. Significant oil recovery values were predicted, both for injection of polymer solutions and surfactant-polymer solutions and with both horizontal and vertical wells. Surprisingly, addition of surfactant provides lower oil recovery than for polymer flooding alone (under same injection slug size, when all subdivisions were considered in the model). This result appeared to occur because the thermodynamics build into models allows the surfactant formulation to freeze easier than the polymer solution without surfactant. This freezing depletes the surfactant bank, and therefore, lowers oil recovery. On the other hand, this freezing actually promotes growth of the permafrost, whereas, injection of polymer alone causes a mild thawing of the permafrost. One might question whether the thermodynamics built into the simulator are correct, but this result does emphasis that in addition to temperature, the chemistry of the injected formulation may be important in determining the fate of the permafrost. At a certain well distance to permafrost (1,640 ft), horizontal injection wells cause greater thawing of permafrost than vertical wells, when wellbores are close to the taliks. Higher concentration and viscosity of polymer slugs have small potential for thawing permafrost, largely because of the injectivity reduction during polymer flooding (thus allowing slower heat dissipation). Examination of polymer injection as a function of pressure, temperature, and mean stress, suggests that subsidence of permafrost could be negligible. The effects on permafrost subsidence increases modestly as the polymer slug size increases, and decreases modestly as the surfactant-polymer slug size increases. As huge heavy oil reserves exist in Canada and Alaska's North Slope regions, continued resource development in these regions is likely. Therefore, a thorough understanding is required in considering the long-term impact on permafrost stability with the use of modern EOR processes implemented in this unique environment.
Schumi, Bettina (OMV E&P) | Clemens, Torsten (OMV E&P) | Wegner, Jonas (HOT Microfluidics) | Ganzer, Leonhard (Clausthal University of Technology) | Kaiser, Anton (Clariant) | Hincapie, Rafael E. (OMV E&P) | Leitenmüller, Verena (Montan University Leoben)
Chemical Enhanced Oil Recovery leads to substantial incremental costs over waterflooding of oil reservoirs. Reservoirs containing oil with a high Total Acid Number (TAN) could be produced by injection of alkali. Alkali might lead to generation of soaps and emulsify the oil. However, the generated emulsions are not always stable.
Phase experiments are used to determine the initial amount of emulsions generated and their stability if measured over time. Based on the phase experiments, the minimum concentration of alkali can be determined and the concentration of alkali above which no significant increase in formation of initial emulsions is observed.
Micro-model experiments are performed to investigate the effects on pore scale. For injection of alkali into high TAN number oils, mobilization of residual oil after waterflooding is seen. The oil mobilization is due to breaking-up of oil ganglia or movement of elongated ganglia through the porous medium. As the oil is depleting in surface active components, residual oil saturation is left behind either as isolated ganglia or in down-gradient of grains.
Simultaneous injection of alkali and polymers leads to higher incremental oil production in the micro-models owing to larger pressure drops over the oil ganglia and more effective mobilization accordingly.
Core flood tests confirm the micro-model experiments and additional data are derived from these tests. Alkali co-solvent polymer injection leads to the highest incremental oil recovery of the chemical agents which is difficult to differentiate in micro-model experiments. The polymer adsorption is substantially reduced if alkali is injected with polymers compared with polymer injection only. The reason is the effect of the pH on the polymers. As in the micro-models, the incremental oil recovery is also higher for alkali polymer injection than with alkali injection only.
To evaluate the incremental operating costs of the chemical agents, Equivalent Utility Factors (EqUF) are calculated. The EqUF takes the costs of the various chemicals into account. The lowest EqUF and hence lowest chemical incremental OPEX are incurred by injection of Na2CO3, however, the highest incremental recovery factor is seen with alkali co-solvent polymer injection. It should be noted that the incremental oil recovery owing to macroscopic sweep efficiency improvement by polymer needs to be taken into account to assess the efficiency of the chemical agents.