The artificial lift system (AL) is the most efficient production technique in optimizing production from unconventional horizontal oil and gas wells. Nonetheless, due to declining reservoir pressure during the production life of a well, artificial lifting of oil and gas remains a critical issue. Notwithstanding the attempt by several studies in the past few decades to understand and develop cutting-edge technologies to optimize the application of artificial lift in tight formations, there remains differing assessments of the best approach, AL type, optimum time and conditions to install artificial lift during the life of a well. This report presents a comprehensive review of artificial lift systems application with specific focus on tight oil and gas formations across the world. The review focuses on thirty-three (33) successful and unsuccessful fieldtests in unconventional horizontal wells over the past few decades. The purpose is to apprise the industry and academic researchers on the various AL optimization approaches that have been used and suggest AL optimization areas where new technologies can be developed.
Quintero, Harvey (ChemTerra Innovation) | Abedini, Ali (Interface Fluidics Limited) | Mattucci, Mike (ChemTerra Innovation) | O’Neil, Bill (ChemTerra Innovation) | Wust, Raphael (AGAT Laboratories) | Hawkes, Robert (Trican Well Service LTD) | De Hass, Thomas (Interface Fluidics Limited) | Toor, Am (Interface Fluidics Limited)
For optimizing and enhancing hydrocarbon recovery from unconventional plays, the technological race is currently focused on development and production of state-of-the-art surfactants that reduce interfacial tension to mitigate obstructive capillary forces and thus increase the relative permeability to hydrocarbon (
A heterogeneous dual-porosity dual-permeability microfluidic chip was designed and developed with pore geometries representing shale formations. This micro-chip simulated Wolfcamp shale with two distinct regions: (i) a high-permeability fracture zone (20 µm pore size) interconnected to (ii) a low-permeability nano-network zone (100 nm size). The fluorescent microscopy technique was applied to visualize and quantify the performance of different flowback enhancers during injection and flowback processes.
This study highlights results from the nanofluidic analysis performed on Wolfcamp Formation rock specimens using a microreservoir-on-a-chip, which showed the benefits of the multi-functionalized surfactant (MFS) in terms of enhancing oil/condensate production. Test results obtained at a simulated reservoir temperature of 113°F (45°C) and a testing pressure of 2,176 psi (15 MPa) showed a significant improvement in relative permeability to hydrocarbon (
Measurements using a high-resolution spinning drop tensiometer showed a 40-fold reduction in interfacial tension when the stimulation fluid containing MFS was tested against Wolfcamp crude at 113°F (45°C). Also, MFS outperformed other premium surfactants in Amott spontaneous imbibition analysis when tested with Wolfcamp core samples.
This work used a nanofluidic model that appropriately reflected the inherent nanoconfinement of shale/tight formation to resolve the flowback process in hydraulic fracturing, and it is the first of its kind to visualize the mechanism behind this process at nanoscale. This platform also demonstrated a cost-effective alternative to coreflood testing for evaluating the effect of chemical additives on the flowback process. Conventional lab and field data were correlated with the nanofluidic analysis.
Many investigations have been discussed and it is a well-recognized fact that sonic wave velocity is not only influenced by its rock matrix and the fluids occupying the pores but also by the pore architecture details of the rock bulk. This situation still brings a lack of understanding, and this study is purposed to clearly explain how acoustic velocity and quality factor correlate with porosity, permeability and details internal pore structure in porous rocks.
This study employs 67 sandstone and 120 carbonate core samples collected from several countries in Europe, Australia, Asia, and USA. The measured values are available for porosity
At least eight rock groups are established from rock typing with its Kozeny constant. This constant is a product of pore shape factor
As a novelty, the empirical equations are derived to estimate compressional velocity and quality factor based on petrophysical parameters. Furthermore, this study also establishes empirical equations for predicting porosity and permeability by using compressional wave velocity, critical porosity, and PGS rock typing.
Intelligent multilateral well completions provide downhole flow rate, pressure, and temperature measurements at multiple well segments which allows for a continuous spatiotemporal data stream. Such an extensive data input poses a challenging task to decide on the optimal strategy of manipulating the inflow control valve (ICV) settings over time for best performance. This study investigated the use of machine learning to analyze and predict well performance given different ICV settings to ultimately maximize the well output.
A commercial reservoir simulator was used to generate two synthetic reservoir models: homogeneous (Case A) and heterogenous (Case B). These synthetic data were used to train, validate, and test machine learning models. The reservoir cases were generated based on a segmented, trilateral producer completed with three ICV devices installed at tie-in segments. The data used were measurements of wellhead and downhole flow rates across ICV segments over a period of 4,000 days. A total of 1,330 experiments were conducted with an eight-day timestep, generating a total of 667,660 sample data points for each of Case A and Case B. Fully connected neural networks were used to fit the data while model generalizability was enhanced using regularization techniques, namely L2 regularization and early stopping.
Both random sampling and Latin Hypercube Sampling (LHS) methods were evaluated in constructing the training, validation, and testing splits. Trained with different sample sizes drawn from the 1,330 simulated data histories for the two reservoir models, the proposed neural network showed excellent results. Given only ten simulated choices of ICV settings for training, the network proved capable of predicting oil and water production profiles at surface for both homogeneous and heterogeneous reservoir models with over 0.95 coefficient of determination (R2) when evaluated at unseen, test ICV settings. Extending the problem to downhole flow performance prediction, about 40 training simulated settings were necessary to achieve 0.95 R2. We observed that LHS was superior to random sampling in both R2 average and confidence interval. We also found that increasing the training and validation sample sizes increased the test R2 when testing against unseen cases. Study results suggest the applicability of machine reinforcement learning to estimate the well output at different ICV settings, where the neural network model depends fully on the real-time well feedback and production measurements.
By using a machine learning approach during the operation of a well with multiple ICV settings, it would be feasible to estimate the lateral-by-lateral output at unseen scenarios. Hence, it becomes possible to maximize the well output by using an optimization algorithm to determine the optimal ICV settings.
A sizeable portion of the Athabasca oil sand reservoir is classified as Inclined Heterolithic Stratification lithosomes (IHSs). However, due to the significant heterogeneity of IHSs and the minimal experimental studies on them, their hydro-geomechanical properties are relatively unknown. The main objectives of this study are investigating the geomechanical constitutive behavior of IHSs and linking their geological and mechanical characteristics to their hydraulic behavior to estimate the permeability evolution of IHSs during a Steam Assisted Gravity Drainage (SAGD) operation. To that end, a detailed methodology for reconstitution of analog IHS specimens was developed, and a microscopic comparative study was conducted between analog and in situ IHS samples. The SAGD-induced stress paths were experimentally simulated by running isotropic cyclic consolidation and drained triaxial shearing tests on analog IHSs. Both series of experiments were performed in conjunction with permeability tests at different strain levels, flow rates, and stress states. Additionally, an analog sample with bioturbation was tested to examine the hydro-geomechanical effects of bioturbation. Finally, the hydro-mechanical characteristics of analog IHS were compared with its constituent layers (sand and mud).
The microscopic study showed that the layers’ integration and grain size distribution are similar in analog and in-situ IHS specimens. The results also revealed that geomechanical properties of IHSs, such as shear strength, bulk compressibility, Young's modulus, and dilation angle, are stress state dependent. In other words, elevating confining pressure could significantly increase the strength and elastic modulus of a sample, while decreasing the compressibility and dilation angle. In contrast, the friction angle and Poisson's ratio are not very sensitive to changes in the isotropic confining stress. An important finding of this study is that the effect of an IHS sample's volume change on permeability is contingent on the stress state and stress path. Volume change during isotropic unloading-reloading resulted in permeability increases and sample dilation during compression shearing resulted in permeability decreases, especially at high effective confining stresses. Moreover, the tests revealed that the existence of bioturbation dramatically improves permeability of IHSs in comparison to equivalent non-bioturbated specimens but has negligible effects on its mechanical properties, which remain similar to non-bioturbated specimens. The results also showed that bioturbation has minimal impact on permeability changes during shearing. Lastly, experimental correlations were developed for each of the parameters mentioned above.
For the first time, specialized experimental protocols have been developed that guide the infrastructure and processes required to reconstitute analog IHS specimens and conduct geomechanical testing on them. This study also delivered fundamental constitutive data to better understand the geomechanical behavior of IHS reservoir and its permeability evolution during the in-situ recovery processes. Such data can be used to accurately capture the reservoir behavior and increase the efficiency of SAGD operations in IHS reservoirs.
This month's SPE Spotlight on Young Professionals highlights George Buslaev, head of the drilling department at Ukhta State Technical University. Ukhta is an important industrial town in the Timan-Pechora Basin of Russia and has a long-standing history in the oil and gas industry. Being the son of a scientist-engineer who initiated directional drilling in the Timan-Pechora oil and gas province, Buslaev has always had a passion for the petroleum industry, receiving his PhD from the Ukhta State Technical University. Through his hard work and determination he has published 57 works, including 6 patents and 6 SPE papers. His engagement in a variety of fields made SPE become an integral part of his life.
This study examines which is the margin of usability for Artificial Intelligence (AI) algorithms related to the rock properties distribution in static modeling. This novel method shows a forward modeling approach using neural networks and genetic algorithms to optimize correlation patterns among seismic traces of stack volumes and well rock properties. Once a set of nonlinear functions is optimized in the well locations, to correlate seismic traces and rock properties, spatial response is estimated using the seismic volume. This seismic characterization process is directly dependent on the error minimization during the structural seismic interpretation process, as well as, honoring the structural complexity while modeling. Previous points are key elements to obtain an adequate correlation between well data and seismic traces. The joint mechanism of neural networks and genetic algorithms globally optimize the nonlinear functions and its parameters to minimize the cost function. Estimated objective function correlates well rock properties with seismic stack data. This mechanism is applied to real data, within a high structural complexity and several wells. As an output, calibrated petrophysical time volumes in the interval of interest are obtained. Properties are used initially to generate a geological facies model. Subsequently, facies and seismic properties are used for the three-dimensional distribution of petrophysical properties such as: rock type, porosity, clay volume and permeability. Therefore, artificial intelligence algorithms can be widely exploited for uncertainty reduction within the rock property spatial estimation.
The major challenge facing society in the 21st century is to improve the quality of life for all citizens in an egalitarian way, by providing sufficient food, shelter, energy and other resources for a healthy meaningful life, whilst at the same time decarbonizing anthropogenic activity to provide a safe global climate. This means limiting the temperature rise to below 2 C. Currently, spreading wealth and health across the globe is dependent on growing the GDP of all countries. This is driven by the use of energy, which until recently has mostly derived from fossil fuel, though a number of countries have shown a decoupling of GDP growth and greenhouse gas emissions from the energy sector through rapid increases in low carbon energy generation. Nevertheless, as low carbon energy technologies are implemented over the coming decades, fossil fuels will continue to have a vital role in providing energy to drive the global economy. Considering the current level of energy consumption and projected implementation rates of low carbon energy production, a considerable quantity of fossil fuels will still be used, and to avoid emissions of GHG, carbon capture and storage (CCS) on an industrial scale will be required. In addition, the IPCC estimate that large scale GHG removal from the atmosphere is required using technologies such as Bioenergy CCS to achieve climate safety. In this paper we estimate the amount of carbon dioxide that will have to be captured and stored, the storage volume and infrastructure required if we are to achieve both the energy consumption and GHG emission goals. By reference to the UK we conclude that the oil and gas production industry alone has the geological and engineering expertise and global reach to find the geological storage structures and build the facilities, pipelines and wells required. Here we consider why and how oil and gas companies will need to morph into hydrocarbon production and carbon dioxide storage enterprises, and thus be economically sustainable businesses in the long term, by diversifying in and developing this new industry.
The evolution of hydraulic fracturing is a long and circuitous one that deserves examination. Engineering and completions leaders from Liberty Oilfield Services did just that, authoring a paper that encapsulates the high points in the development of the groundbreaking completions practice. Producers in Texas have claimed an economic victory with their transition to local sands that they once avoided using in horizontal wells due to their low-quality. Driven by a recovery in well completions and increased proppant loading per well, the market for raw fracturing sand is expected to grow by more than 4% annually through 2021, an industry research study says. Permian Basin producer Callon Petroleum is attributing its data-driven approach to a routine completions practice to improved proppant placement and higher oil production.
In need of an exploration boost, Norway doled out a record 83 production licenses in mature areas of the Norwegian Continental Shelf to 33 firms. Norway hopes for a continued rise in offshore exploration and development activity to ensure steady oil and gas production through the next decade. Equinor has grabbed seven new licenses in the Barents and Norwegian Seas, the latest in a flurry of offshore activity in which the firm has added acreage off the UK and Brazil, gained approval for a big Arctic project, and awarded billions of dollars in service contracts. A reservoir-conditions coreflood study was undertaken to assist with design of drilling and completion fluids for a Norwegian field. Multiple fluids were tested, and the lowest permeability alterations did not correlate with the lowest drilling-fluid-filtrate-loss volumes.