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.
Temizel, Cenk (Aera Energy) | Canbaz, Celal Hakan (Ege University) | Palabiyik, Yildiray (Istanbul Technical University) | Putra, Dike (Rafflesia Energy) | Asena, Ahmet (Turkish Petroleum Corp.) | Ranjith, Rahul (Far Technologies) | Jongkittinarukorn, Kittiphong (Chulalongkorn University)
Smart field technologies offer outstanding capabilities that increase the efficiency of the oil and gas fields by means of saving time and energy as far as the technologies employed and workforce concerned given that the technology applied is economic for the field of concern. Despite significant acceptance of smart field concept in the industry, there is still ambiguity not only on the incremental benefits but also the criteria and conditions of applicability technical and economic-wise. This study outlines the past, present and the dynamics of the smart oilfield concept, the techniques and methods it bears and employs, technical challenges in the application while addressing the concerns of the oil and gas industry professionals on the use of such technologies in a comprehensive way.
History of smart/intelligent oilfield development, types of technologies used currently in it and those imbibed from other industries are comprehensively reviewed in this paper. In addition, this review takes into account the robustness, applicability and incremental benefits these technologie bring to different types of oilfields under current economic conditions. Real field applications are illustrated with applications in different parts of the world with challenges, advantages and drawbacks discussed and summarized that lead to conclusions on the criteria of application of smart field technologies in an individual field.
Intelligent or Smart field concept has proven itself as a promising area and found vast amount of application in oil and gas fields throughout the world. The key in smart oilfield applications is the suitability of an individual case for such technology in terms of technical and economic aspects. This study outlines the key criteria in the success of smart oilfield applications in a given field that will serve for the future decisions as a comprehensive and collective review of all the aspects of the employed techniques and their usability in specific cases.
Even though there are publications on certain examples of smart oilfield technologies, a comprehensive review that not only outlines all the key elements in one study but also deducts lessons from the real field applications that will shed light on the utilization of the methods in the future applications has been missing, this study will fill this gap.
Mylnikov, Danila (Geosteering Technologies) | Tatur, Olga (Geosteering Technologies) | Sabirov, Airat (Geosteering Technologies) | Brazlauskas, Anton (Geosteering Technologies) | Petrakov, Uriy (Geosteering Technologies) | Sobolev, Alexey (Geosteering Technologies) | Sigarev, Sergei (RN-Uvatneftegaz) | Kustarev, Dmitry (RN-Uvatneftegaz) | Yakovlev, Konstantin (RN-Uvatneftegaz)
The objective of this work was to drill a horizontal well in the Bazhen formation (Western Siberia, Severo-Demyanskoye field) in order to achieve the maximum possible efficiency both from technological and economical points of view. To solve the task, it was decided to apply a integrated approach to drilling support: synchronously use the methods of geosteering, the results of geomechanical modeling and evaluation of the reservoir properties during the drilling process. The experience of drilling support accumulated by the company at the beginning of the work demonstrated that the coordinated use of the three disciplines leads to an increase in the efficiency of the well construction process.
Pre-drill geomechanical models of the geological interval of interest were constructed for the target well, and also reservoir properties were evaluated. The wellbore stability analysis is carried out and intervals with high risks of destruction of a breed on walls of a chink are revealed. To minimize risks, recommendations were given on the technological parameters of the well construction: mud weight, casing shoe depth, etc.
Based on the actually measured rock properties, the petrophysical characteristics of the formation were evaluated. Geosteering methods were used to correct the trajectory in real time for the most efficient well placement in the target interval with the best reservoir properties. Every time the trajectory was changed, the geomechanical model was rebuilt, demonstrating an updated risk map. As a result, the well trajectory was adjusted taking into account minimization of unstable borehole risks.
Anokhina, E. (Immanuel Kant Baltic Federal University) | Erokhin, G. (Immanuel Kant Baltic Federal University) | Kirichek, A. (Immanuel Kant Baltic Federal University) | Nazarova, M. (Immanuel Kant Baltic Federal University)
Scattered waves detection of fractured zones in the high salinity waters dolomite layers of the Lower Cambrian saliferous-carbonaceous complex which are characterized by higher than normal formation pressures. When hole boring in the fractured zones, catastrophic drilling mud absorption, gas ingress, casing columns crush in some wells are observed. Identification of such zones when designing and building wells will allow avoiding complications and damages while drilling.
Today, it has become pretty obvious that the rocks that were conventionally believed to be source rocks are, in many cases, oil bearing rocks. This oil bearing potential can be attributed to both filtration of the hydrocarbon fluids in the reservoir and artificial generation of liquid and gaseous hydrocarbons by heating the solid organic matter underground. The suggested differentiated approach to estimation of the resource base allows identifying and assessing oil bearing potentials for various reservoir stimulation techniques: drawdown (due to natural reservoir properties of the rock), creation of artificial permeability, and generation of hydrocarbons by in-situ pyrolysis of the organic matter.
Kahrobaei, Siavash (Delft University of Technology) | Habibabadi, M. Mansoori (Delft University of Technology) | Joosten, Gerard J. P. (Sharif University of Technology) | Van den Hof, Paul M. J. (Shell Global Solutions International) | Jansen, Jan-Dirk (Eindhoven University of Technology)
Classic identifiability analysis of flow barriers in incompressible single-phase flow reveals that it is not possible to identify the location and permeability of low-permeability barriers from production data (wellbore pressures and rates), and that only averaged reservoir properties in between wells can be identified. We extend the classic analysis by including compressibility effects. We use two approaches: a twin experiment with synthetic production data for use with a time-domain parameter-estimation technique, and a transfer-function formalism in the form of bilaterally coupled four-ports allowing for an analysis in the frequency domain. We investigate the identifiability, from noisy production data, of the location and the magnitude of a low-permeability barrier to slightly compressible flow in a 1D configuration. We use an unregularized adjoint-based optimization scheme for the numerical time-domain estimation, by use of various levels of sensor noise, and confirm the results by use of the semianalytical transfer-function approach. Both the numerical and semianalytical results show that it is possible to identify the location and the magnitude of the permeability in the barrier from noise-free data. By introducing increasingly higher noise levels, the identifiability gradually deteriorates, but the location of the barrier remains identifiable for much-higher noise levels than the permeability. The shape of the objective-function surface, in normalized variables, indeed indicates a much-higher sensitivity of the well data to the location of the barrier than to its magnitude. These theoretical results appear to support the empirical finding that unregularized gradient-based history matching in large reservoir models, which is well-known to be a severely ill-posed problem, occasionally leads to useful results in the form of model-parameter updates with unrealistic magnitudes but indicating the correct location of model deficiencies.
ASP flooding achieves high incremental oil recovery factors over water flooding by reducing the interfacial tension (IFT) to ultralow values and by ensuring good mobility control, provided by the polymer. Traditionally, this has been achieved by tuning the ASP flood so that it is at optimum salinity conditions, i.e. Winsor type III micro-emulsion phase. Systematic studies of the performance of ASP at different (non-optimum) salinities are scarce, while operating at lower salinities condition can offer several advantages. These include: (1) lower surfactant retention and (2) increased polymer viscosifying power, enabling a reduction in required chemical volumes, as well as (3) a lower risk of achieving over-optimum salinity conditions in the field. This paper presents a series of core-flood experiments using light crude oil with a low Total Acid Number (TAN) and two different sandstone rock types (Bentheimer and Berea). Injection salinities ranged from under-optimum to optimum conditions (i.e. giving type II- to type III micro-emulsion systems), supported by phase behaviour and spinning drop IFT measurements. The formulation used was a model, non-optimized one with one internal olefin sulfonate (IOS) surfactant component. The injected ASP solution showed no phase separation but it was not clear.
Results for this IOS surfactant system, without the addition of extra components such as a co-surfactant for improved aqueous solubility, show that ASP core flood tests performed at different salinities, both at optimum salinity and up to 1.5% NaCl under-optimum, recovered similar amounts of oil remaining in the core after water flooding, regardless of a factor three difference in IFT within the range of 10-3 and 10-2 mN/m. The residual oil saturation after chemical flooding (Sorc) was similar amongst the different experiments, ranging from 16% up to 19% Pore Volume (PV) for our specific model formulation. Moreover, oil and chemical breakthrough times are in the same range for all experiments: around 0.5 PV and 1 PV, respectively. Although total oil recovery was not affected by flooding at under-optimum conditions, lower surfactant retention and a higher oil recovery before chemical breakthrough (i.e. as clean oil) were found. In the absence of a surfactant (AP flood), poor recovery of residual oil after water flooding, regardless of a factor three difference in IFT within the range of 10-3 and 10-2 mN/m. The residual oil saturation after chemical flooding (Sorc) was similar amongst the different experiments, from 16% up to 19% Pore Volume (PV) for our specific model formulation. Moreover, oil and chemical breakthrough times are in the same range for all experiments: around 0.5 PV and 1 PV, respectively. Although total oil recovery was not affected by flooding at under-optimum conditions, lower surfactant retention and a higher oil recovery before chemical breakthrough (i.e. as clean oil) were found. In the absence of a surfactant (AP flood), poor recovery of residual oil after water flood was achieved (Sorc 32% PV). These findings suggest that injection at under-optimum conditions may be, for an IOS surfactant system, an improved, alternative to injecting at optimum conditions. Further work is recommended to quantify its advantages, including with more aqueous soluble optimized surfactant systems.
Describe the energy production and consumption in the Middle East.
Energy demand in the Middle East is expected to increase by 60% by 2035. Despite the fact that the Middle East holds more than 40% of the global oil and gas reserves, meeting local and global future energy demands will be a major challenge.
It is evident that the global energy system will go through a major transition this century. Shell expects the global energy supply mix to evolve significantly in the decades ahead, with gas, the cleanest burning fossil fuel, becoming more widely used for power generation. While we expect renewables such as wind, solar, and biofuels to play an increasingly important role, oil and gas will be vital to meet the considerable expected increases in energy demand. Building a sustainable energy future is a complex challenge. Irrespective of how long the transition will last, technology innovation will be a crucial driver and will play a vital role to facilitate the transition.
It is imperative for our industry that we maximize the economic ultimate recovery from our existing fields. With the global average field recovery from waterflooding currently being around 35%, leaving from 60% to 70% of the oil in place is a very large opportunity. However, many current and future development opportunities are dominated by complex improved oil recovery (IOR) and enhanced oil recovery (EOR) projects, challenging fluids (heavy oil and sour gas), challenging reservoirs (tight formations, deep reservoirs, high temperature and high pressure), or challenging environmental settings (deep water and Arctic).
Shell has been deeply committed to EOR technology deployment for more than 40 years, throughout the time that EOR has been featured on the industry agenda. In-depth knowledge and an extensive tool box of proven and novel recovery technology solutions are the result.
The Middle East is an important region for Shell and we are proud of the many long-standing partnerships we have established. We have worked with Petroleum Development Oman (PDO) for many decades. Our relationship with the UAE dates back more than 75 years. We are also involved in major projects elsewhere in the region: in Qatar, Saudi Arabia, Kuwait, Egypt, and Iraq.
Saputelli, Luigi (Frontender Corporation) | Verde, Alexander (Frontender Corporation) | Haris, Zameel (Frontender Corporation) | Díaz, Daniel (Frontender Corporation) | Diaz, Daniel (now with Geopark Colombia)
Decline curve analysis models are adequate for unconventional field production forecasting as a function of wells scheduling and high-level screening of reservoir capacity, depletion scenarios and market needs, however those models do not consider rigorous physics since they assume constant production conditions. For complex field development and production operations optimization, integrated reservoir performance models constrained with well and surface network pressure must be considered. Alternatively, numeric simulation-driven forecasting methods provide an advanced level of subsurface response however they require intensive model tuning effort which may not be practical for a large number of wells with limited reservoir data.
The objective of this effort was to develop an automated workflow to generate production forecasts in the context of integrated reservoir to facilities production modeling. We leveraged on reservoir analytic models and gradient based optimization to identify associated model parameters. Since gradient-based optimization required the specification of an initial guess, we used case-based reasoning to focus on the most relevant parameters and to select better initial guesses a smaller solution range to narrow possible solutions.
The proposed solution was successfully tested in a large tight-gas field with approximately 250 wells and a long production history. For this field, rate-transient-analysis models provided certain advantages as they captured field performance response using pressure vs. rate fundamental modeling with the tuning of few parameters. Decline curve models were used as inputs to derive a full physics-based reservoir and well performance models therefore translating time-dependent models into fully pressure and time dependent models. The identified parameters, such as reservoir pressure, well permeability and well drainage radius provided the means to generate full physics well responsive models. The calibrated reservoir and well models were able to reproduce production history with minimum error as well as to provide a means to optimize production using an integrated reservoir, well and facilities production model, which were not possible using decline curve models alone.