The US Department of Energy (DOE) has announced the selection of six projects to receive approximately $30 million in federal funding for cost-shared research and development in unconventional oil and natural gas recovery. The projects, selected under the Office of Fossil Energy's Advanced Technology Solutions for Unconventional Oil and Gas Development funding opportunity, will address critical gaps in the understanding of reservoir behavior and optimal well-completion strategies, next-generation subsurface diagnostic technologies, and advanced offshore technologies. As part of the funding opportunity announcement and at the direction of Congress, DOE solicited field projects in emerging unconventional plays with less than 50,000 B/D of current production, such as the Tuscaloosa Marine Shale and the Huron Shale. The newly selected projects will help master oil and gas development in these types of rising shales. This cement will prevent offshore spills and leakages at extreme high-temperature, high-pressure, and corrosive conditions.
With the advent of high-resolution methods to predict hydraulic fracture geometry and subsequent production forecasting, characterization of productive shale volume and evaluating completion design economics through science-based forward modeling becomes possible. However, operationalizing a simulation-based workflow to optimize design to keep up with the field operation schedule remains the biggest challenge owing to the slow model-to-design turnaround cycle. The objective of this project is to apply the ensemble learning-based model concept to this issue and, for the purpose of completion design, we summarize the numerical-model-centric unconventional workflow as a process that ultimately models production from a well pad (of multiple horizontal laterals) as a function of completion design parameters. After the development and validation and analysis of the surrogate model is completed, the model can be used in the predictive mode to respond to the "what if" questions that are raised by the reservoir/completion management team.
A new real-time machine learning model has been developed based on the deep recurrent neural network (DRNN) model for performing step-down analysis during the hydraulic fracturing process. During a stage of the stimulation process, fluids are inserted at the top of the wellhead, while the flow is primarily driven by the difference between the bottomhole pressure (BHP) and reservoir pressure. The major physics and engineering aspects involved are complex and, quite often, there is a high level of uncertainty related to the accuracy of the measured data, as well as intrinsic noise. Consequently, using a machine learning-based method that can resolve both the temporal and spatial non-linear variations has advantages over a pure engineering model.
The approach followed provides a long short-term memory (LSTM) network-based methodology to predict BHP and temperature in a fracturing job, considering all commonly known surface variables. The surface pumping data consists of real-time data captured within each stage, such as surface treating pressure, fluid pumping rate, and proppant rate. The accurate prediction of a response variable, such as BHP, is important because it provides the basis for decisions made in several well treatment applications, such as hydraulic fracturing and matrix acidizing, to ensure success.
Limitations of the currently available modeling methods include low resolution BHP predictions and an inability to properly capture non-linear effects in the BHP/temperature time series relationship with other variables, including surface pressure, flow rate, and proppant rate. In addition, current methods are further limited by lack of accuracy in the models for fluid properties; the response of the important sub-surface variables strongly depends on the modeled fluid properties.
The novel model presented in this paper uses a deep learning neural network model to predict the BHP and temperature, based on surface pressure, flow rate, and proppant rate. This is the first attempt to predict response variables, such as BHP and temperature, in real time during a pumping stage, using a memory-preserving recurrent neural network (RNN) variant, such as LSTM. The results show that the LSTM can successfully model the BHP and temperature in a hydraulic fracturing process. The BHP and temperature predictions obtained were within 5% relative error. The current effort to model BHP can be used for step-down analysis in real time, thereby providing an accurate representation of the subsurface conditions in the wellbore and in the reservoir. The new method described in this paper avoids the need to manage the complex physics of the present methods; it provides a robust, stable, and accurate numerical solution throughout the pumping stages. The method described in this paper is extended to manage step-down analysis using surface-measured variables to predict perforation and tortuosity friction.
Texas regulators rejected a rare challenge to gas flaring in the state after an oil company argued that a flaring ban would force it to shut in wells, damaging the reservoir and reducing future oil production. There is every reason to believe that enhanced oil recovery through huff-and-puff injections in US tight-oil plays could be a technical success across large numbers of wells. However, widespread economic success remains uncertain. This paper demonstrates how engineers can take advantage of their most-detailed completions and geomechanical data by identifying trends arising from past detailed treatment analyses. This paper studies the technical and economic viability of this EOR technique in Eagle Ford shale reservoirs using natural gas injection, generally after some period of primary depletion, typically through long, hydraulically fractured horizontal-reach wells.
Integrated surveillance is critical for understanding reservoir dynamics and improving field management. A key component of the surveillance is areal monitoring of subsurface changes by use of time-lapse geophysical surveys such as 4D seismic. The purpose of the complete paper is to create a performance-based reservoir characterization by use of production data (measured rates and pressures) from a selected gas-condensate region within the Eagle Ford Shale.
Digital technologies serve as a primary theme of this year’s group, with a few environmentally conscious firms included in the mix. The large independent put together a team of data scientists, software developers, and petrotechnical staff to create a forward-looking vision for how to use digital technology to solve problems. Do women in academia face the same challenges as their peers in industry? Using maglev technology, a new artificial lift system seeks to boost production output by sucking down reservoir pressure from inside the wellbore and from inside the reservoir. The projects are designed to reduce technical risks in enhanced oil recovery and expand application of EOR methods in conventional and unconventional reservoirs.
Colorado Gov. John Hickenlooper recently said the home explosion in Firestone, Colorado, that killed two people was a “freak accident.” But a new study by the Colorado School of Public Health indicates that accidents like this may not be so uncommon. The company that owns a gas well linked to a fatal home explosion in Colorado says it will permanently shut down that well and two others in the neighborhood. Anadarko Petroleum announced on its website on 16 May that it is permanently disconnecting 1-in.-diameter
Anadarko Petroleum wants a fleet of at least six vehicles with armor heavy enough to stop AK-47 bullets at its natural-gas project in Mozambique. And it needs them soon. Anadarko Petroleum said late on 30 June that it has tested more than 4,000 active oil and gas lines and plugged another 2,400 inactive ones per a state order issued after a fatal home explosion in Firestone, Colorado, in April. Two months after a Colorado home exploded near an Anadarko well, the reverberations are still rattling the oil industry, driving down driller shares and raising fears of a regulatory backlash. The company that owns a gas well linked to a fatal home explosion in Colorado says it will permanently shut down that well and two others in the neighborhood.
This paper studies the technical and economic viability of this EOR technique in Eagle Ford shale reservoirs using natural gas injection, generally after some period of primary depletion, typically through long, hydraulically fractured horizontal-reach wells. The Eagle Ford formation has produced approximately 2 billion bbl of oil during the last 7 years, yet its potential may be even greater. Using improved oil-recovery (IOR) methods could result in billions of additional barrels of production. Shale EOR Works, But Will It Make a Difference? The promise of getting 30% more oil production from shale wells has set off a race by companies trying to see if they can replicate what EOG has done.
This paper studies the technical and economic viability of this EOR technique in Eagle Ford shale reservoirs using natural gas injection, generally after some period of primary depletion, typically through long, hydraulically fractured horizontal-reach wells. The Eagle Ford formation has produced approximately 2 billion bbl of oil during the last 7 years, yet its potential may be even greater. Using improved oil-recovery (IOR) methods could result in billions of additional barrels of production.