Hydrocarbons are trapped at great depths with pressure and temperature higher than surface conditions which would vary depending on reservoir properties. When the well is set on production, these hydrocarbons travel through the wellbore over reducing geothermal and formation pressure gradients. Hence, at shallower depths the temperature drops below the cloud point and sometimes, below pour point of crude thus creating an ambient temperature for the formation of wax and deposition of paraffin on the inner side of production tubing.
It has been observed that when hot fluid passes through a pipe which is covered by a continuously circulating hot water bath, the temperature difference of the fluid at surface outlet and sub-surface reservoir is reduced to a minimal value. This paper therefore proposes a practical application of such heat transfer within a wellbore for passively solving major industrial issues of paraffin depositions. The idea lies in minimizing the heat losses, which can be effectively done by insulating the inner side of the casing so that the annulus and fluid flowing within the tubing is isolated from exterior losses. According to the First law of Thermodynamics the fluid flowing within the tubing will experience reduction in thermal gradient. These loses can be compensated by injecting hotter brine through a pipe at the bottom of the annulus, which is isolated, using production packer. Further, circulating hot fluid in the annulus would result in isothermal heating of the fluid flowing through the tube which would minimize the heat loss across tubing, causing an increase in temperature of fluid at the surface above pour point. Several researchers have put forth heat transfer equations across the tubing's, annulus, insulator, casing, cement and the formation which can be used to calculate the overall heat transfer coefficient and thus, the amount of heat losses. Quartz sensors placed at the bottom of a wellbore would detect bottom borehole temperature based on which the injection temperature of fluid can be manipulated. The entire process can be automated by applying an artificial intelligent system which would monitor, control and respond. This method would increase the capex but would decrease the operating cost thus leading to an increase in the life of the well.
Reservoirs which produce under active water drive offer a significant uncertainty towards implementation of Chemical EOR processes. This paper describes a successful pilot testing of ASP process in a clastic reservoir which is operating under strong aquifer drive. The field has ~ 30 years of production history. The objective of the pilot was to understand response of ASP process in a mature reservoir, which is operating under active edge water drive. The build-up permeability of the reservoir is 2-8 Darcy with viscosity~ 50 cP. Salient key observations like production performance, incremental oil gain, polymer breakthrough etc. are discussed in this paper after completion of the pilot.
On the basis of laboratory study and simulation, ASP pilot was implemented in the field in 2010.The pilot was designed with single inverted five spot pattern and one observation well. The pilot envisaged injection of 0.3 pore volume (PV) Alkali-Surfactant-Polymer (ASP) slug, 0.3 PV graded polymer buffer followed by 0.4PV chase water. The pilot was meticulously monitored for production performance and breakthrough of chemicals. All the pilot producers have more than 20 years of production history. Base oil rate and water cut were fixed before start of the pilot, on the basis of test data which was used to monitor pilot performance. Interwell Tracer Test (IWTT) was conducted before starting of ASP injection so as to understand sweep in the pilot area. In addition, quality of injection water and chemical concentration in ASP slug was checked regularly to ensure best quality.
Significant response of the pilot was observed within 15 months of the start of the pilot which was published in 2012. This paper aims to describe the learning and conclusion after successful completion of the pilot. ~40-50% jump in oil rate was observed during the ASP injection period which sustained for 12-18 months. However preferential breakthrough of ASP slug in one of the producer impacted the incremental oil gain. The preferential breakthrough of polymer was due to presence of high permeability streaks which was rectified by profile modification job. In addition, strong aquifer movement was experienced during ASP injection which leads to rise in water cut of a pilot well. However, the pilot well was restored through water shutoff jobs. After completion of ASP and mobility buffer, a cumulative incremental oil ~28000 m3 was obtained. Cumulative incremental oil gain is in line with simulation studies prediction. 12-14% decrease in water cut was observed which sustained for ~ 6-18 months. Regular monitoring of produced fluid indicated breakthrough of polymer and alkali in 2-3 producers. During the pilot, produced fluid handling issues like tough emulsion formation, lift malfunctioning etc. was not observed. These collective observation indicated success of the ASP pilot project.
There are very few case histories of successful ASP pilot implementation are available, in which the reservoirs has been operating under active aquifer drive. Learning of this ASP project can be taken forward for expansion of ASP flood and also designing of ASP pilot/commercial projects for analogous reservoirs.
Temizel, Cenk (Aera Energy) | Balaji, Karthik (University of North Dakota) | Canbaz, Celal Hakan (Ege University) | Palabiyik, Yildiray (Istanbul Technical University) | Moreno, Raul (Smart Recovery) | Rabiei, Minou (University of North Dakota) | Zhou, Zifu (University of North Dakota) | Ranjith, Rahul (Far Technologies)
Due to complex characteristics of shale reservoirs, data-driven techniques offer fast and practical solutions in optimization and better management of shale assets. Developments in data-driven techniques enable robust analysis of not only the primary depletion mechanisms, but also the enhanced oil recovery in unconventionals such as natural gas injection. This study provides a comprehensive background on application of data-driven methods in oil and gas industry, the process, methodology and learnings along with examples of data-driven analysis of natural gas injection in shale oil reservoirs through the use of publicly-available data.
Data is obtained and organized. Patterns in production data are analyzed using data-driven methods to understand key parameters in the recovery process as well as the optimum operational strategies to improve recovery. The complete process is illustrated step-by-step for clarity and to serve as a practical guide for readers. This study also provides information on what other alternative physics-based evaluation methods will be able to offer in the current conditions of data availability and the understanding of physics of recovery in shale oil assets together with the comparison of outcomes of those methods with respect to the data-driven methods. Thereby, a thorough comparison of physics-based and data-driven methods, their advantages, drawbacks and challenges are provided.
It has been observed that data organization and filtering takes significant time before application of the actual data-driven method, yet data-driven methods serve as a practical solution in fields that are mature enough to bear data for analysis as long as the methodology is carefully applied. The advantages, challenges and associated risks of using data-driven methods are also included. The results of comparison between physics-based methods and data-driven methods illustrate the advantages and disadvantages of each method while providing the differences in evaluation and outcome along with a guideline for when to use what kind of strategy and evaluation in an asset.
A comprehensive understanding of the interactions between key components of the formation and the way various elements of an EOR process impact these interactions, is of paramount importance. Among the few existing studies on natural gas injection in shale oil with the use of data-driven methods in oil and gas industry include a comparative approach including the physics-based methods but lack the interrelationship between physics-based and data-driven methods as a complementary and a competitor within the era of rise of unconventionals. This study closes the gap and serves as an up-to-date reference for industry professionals.
Alkinani, Husam H. (Missouri University of Science and Technology) | Al-Hameedi, Abo Taleb T. (Missouri University of Science and Technology) | Dunn-Norman, Shari (Missouri University of Science and Technology) | Alkhamis, Mohammed M. (Missouri University of Science and Technology) | Mutar, Rusul A. (Ministry of Communications and Technology)
Lost circulation is a complicated problem to be predicted with conventional statistical tools. As the drilling environment is getting more complicated nowadays, more advanced techniques such as artificial neural networks (ANNs) are required to help to estimate mud losses prior to drilling. The aim of this work is to estimate mud losses for induced fractures formations prior to drilling to assist the drilling personnel in preparing remedies for this problem prior to entering the losses zone. Once the severity of losses is known, the key drilling parameters can be adjusted to avoid or at least mitigate losses as a proactive approach.
Lost circulation data were extracted from over 1500 wells drilled worldwide. The data were divided into three sets; training, validation, and testing datasets. 60% of the data are used for training, 20% for validation, and 20% for testing. Any ANN consists of the following layers, the input layer, hidden layer(s), and the output layer. A determination of the optimum number of hidden layers and the number of neurons in each hidden layer is required to have the best estimation, this is done using the mean square of error (MSE). A supervised ANNs was created for induced fractures formations. A decision was made to have one hidden layer in the network with ten neurons in the hidden layer. Since there are many training algorithms to choose from, it was necessary to choose the best algorithm for this specific data set. Ten different training algorithms were tested, the Levenberg-Marquardt (LM) algorithm was chosen since it gave the lowest MSE and it had the highest R-squared. The final results showed that the supervised ANN has the ability to predict lost circulation with an overall R-squared of 0.925 for induced fractures formations. This is a very good estimation that will help the drilling personnel prepare remedies before entering the losses zone as well as adjusting the key drilling parameters to avoid or at least mitigate losses as a proactive approach. This ANN can be used globally for any induced fractures formations that are suffering from the lost circulation problem to estimate mud losses.
As the demand for energy increases, the drilling process is becoming more challenging. Thus, more advanced tools such as ANNs are required to better tackle these problems. The ANN built in this paper can be adapted to commercial software that predicts lost circulation for any induced fractures formations globally.
Tangen, Geir Ivan (Lundin Norway AS) | Smaaskjaer, Geir (Lundin Norway AS) | Bergseth, Einar (Lundin Norway AS) | Clark, Andy (Lundin Norway AS) | Fossli, Børre (Enhanced Drilling AS) | Claudey, Eric (Enhanced Drilling AS) | Qiang, Zhizhuang (Enhanced Drilling AS)
In 2015, while coring in the carbonate reservoir in the second appraisal well on an oil and gas discovery in the Barents Sea (386 m water depth), the drill string fell 2 meters and a total mud loss was experienced leading to a well control incident. As a result, since 2016, the operator has introduced and used the Controlled Mud Level (CML) system. To date this system has been used on seven wells including two further appraisal wells on the same field and five exploration wells in the area.
In 2017 it was decided to drill a horizontal well in the same carbonate reservoir and to perform an extended production test in close proximity to the original loss well. Since it is not possible to predict where large voids (karsts) and natural fractures could be encountered, contingency to handle high losses, had to be implemented for the horizontal well. During the well planning, further risk reducing measures were implemented, including the use of wired drill pipe to improve the management of the wellbore pressure profile. This paper describes the planning processes leading up to the operation and the highlights of the operation itself together with the lessons learned. It elaborates on how wired pipe, used in combination with the CML system, added value to the operation. It shows how it was possible to drill the reservoir section with a low overbalance while managing severe losses associated with open karsts and natural fractures and still maintaining the fluid barrier. Despite the severe losses encountered it was possible to safely drill and complete the well without any well control event by use of the CML system.
Simplified analytical methods are used in 1D geomechanics workflows to predict the rock's behavior during drilling, completion and production operations. These methods are simplistic in their approach and enable us in getting a time-efficient solution, however it does lose out on accuracy. In addition, by simplifying equations, we limit our ability to predict behavior of the borehole wall only i.e. near wellbore solutions. Using 1D analytical methods, we are unable to predict full field behavior in response to drilling and production activities. For example, when developing a field wide drilling plan or preparing a field development plan for a complex subsurface setting, a simplified approach may not be accurate enough and on the contrary, can be quite misleading. A 3D numerical solution on the other hand, honours subsurface features of a field and simulates for their effect on stresses. It generates solutions which are more akin to reality.
In this paper, difference between a simplified semi-quantitative well-centric approach (1D) and a full field numerical solution (3D) has been presented and discussed. The subsurface setting considered in this paper is quite complex - high dipping beds with pinch outs and low angled faults in a thrust regime. Wellbore stability and fault stability models have been constructed using well-centric approach and using a full field-wide 3D numerical solution and compared to understand the differences.
In this study, it was clearly observed that field-based approach provided us with more accurate estimation of overburden stresses, variation of pore pressure across the field, changes in stress magnitudes and captured its rotation due to pinch-outs and formation dips. For example, due to variation in topography, the well-centric overburden estimates at the toe of deviated well at reservoir level is lower by 0.21gm/cc as compared to the 3D model. It is also observed that within the field itself stress regime changes from normal to strike slip laterally across the reservoir. In comparison to 1D model, considerable differences in stable mud weight window of upto 1.5ppg is observed in wells located close to faults. This is due to effect of fault on stress magnitude and azimuth. Stress state of 4 faults were assessed and all are estimated to be critically stressed with elevated risk of damaging three wells cutting through. However, a simple 1D assessment of stress state of faults at wells cutting through them, show them to be stable.
Moreover, the 3D geomechanical properties that are input into the numerical simulation also play an important role on the results. The algorithms and data used to populate the properties away from the well, need to be validated and calibrated with the well data, to predict reliable results. As the subsurface was quite complex, and well data was not sampled optimally, both horizontally and vertically, the selection and optimum usage of 3D trends also became crucial.
By comparing the differences between 1D and 3D solutions, importance of 3D numerical modelling over 1D models is highlighted.
Saluja, Vikas (Oil & Natural Gas Corporation LTD.) | Singh, Uday (Oil & Natural Gas Corporation LTD.) | Ghosh, Aninda (Oil & Natural Gas Corporation LTD.) | Prakash, Puja (Oil & Natural Gas Corporation LTD.) | Kumar, Ravendra (Oil & Natural Gas Corporation LTD.) | Verma, Rajeev (Oil & Natural Gas Corporation LTD.)
The case study demonstrated here is the innovative workflow for fault delineation technique on a 3D seismic volume in B-173A Field of Heera Panna Bassein (HPB) Sector, Western Offshore Basin, India. B-173A is located 50 kms west of Mumbai at an average water depth of about 50 m. The field was discovered in the year 1992 and it was put on production in Aug 1998. In B-173A field there are two hydrocarbon bearing zones one is gas bearing Mukta (Lower Oligocene carbonates) Formation and oil bearing Bassein (Middle to Upper Eocene Carbonates) formation.
The present study is an extended workflow on Advanced Seismic Interpretation using Spectral Decomposition and RGB Blending for Fault delineation. Iso-frequency volumes are extracted from Relative Acoustic Impedance data instead of seismic data itself.
The workflow is for effective fault delineation and it consists of Spectral Decomposition of relative acoustic impedance data and RGB Blending of discontinuity attributes of different Iso-frequency volumes.
It is observed that RGB blend volume of discontinuity attributes provided more convincing results for fault delineation as compared to the results of traditional discontinuity attributes.
Mogollón, J. L. (Halliburton) | Yomdo, S. (OIL India Limited) | Salazar, A. (Halliburton) | Dutta, R. (OIL India Limited) | Bobula, D. (Halliburton) | Dhodapkar, P. K. (OIL India Limited) | Lokandwala, T. (Halliburton) | Chandrasekar, V. (CMG)
The perception of better economics and less risk from infill drilling and recompletions are reasons well-focused remedies are preferred compared to reservoir-focused solutions, such as enhanced oil recovery (EOR). However, most literature does not discuss the economic and risk indicators driving this.
Using a real example, this work demonstrates that combining polymer flooding with infill drilling and recompletion substantially increases economic benefits with reasonable risk.
The reservoir considered is an Oligocene sandstone at a depth of 2700 m. The °API is 29.5 and permeability ranges from 50 to 500 mD. Current reservoir pressure is 43% of the original and it is below bubble point. A black oil model with a 133 × 56 × 128 grid was used. The model incorporated more than 50 years of matched primary and waterflooding production history and experimental polymer physico-chemical parameters. For the stochastic economic risks estimation, 1,000 iterations were run for each scenario considering uncertainties in injection-production, capital expenditures (CAPEX), operational expenditures (OPEX), and oil prices.
For a 20-year horizon, the injection-production-pressure profiles were numerically forecasted; economic results were calculated using a classic model and inputs from the forecast. The economic risk was determined stochastically. The redevelopment scenarios considered were as follows: Base: current waterflooding Existing wells interventions: workover, opening shut-in wells, and new perforations Infill drilling: vertical/horizontal infill drilling wells + existing wells operations Polymer flooding: using existing wells Combined Infill and polymer: vertical infill drilling wells and polymer flooding
Base: current waterflooding
Existing wells interventions: workover, opening shut-in wells, and new perforations
Infill drilling: vertical/horizontal infill drilling wells + existing wells operations
Polymer flooding: using existing wells
Combined Infill and polymer: vertical infill drilling wells and polymer flooding
P50 forecasts showed that interventions in existing wells in the base scenario increased oil production by 11% and net present value (NPV) by 71% with a risk index of 0.38.
A numerical optimizer was used to account for possible combinations of 14 potential drilling locations and vertical to horizontal well ratios. A scenario with three vertical wells was selected. Compared to the base case, this scenario showed an oil production increase of 23%, NPV increase of 178%, and a risk index of 0.41.
The injection rate of the polymer flood was optimized, resulting in a 17% increase in oil production and 95% increase in the NPV, with a risk index of 0.40. This justifies performing a polymer flood.
The most promising scenario is the combined infill drilling and polymer injection, which significantly improved the economic indicators—30% increase in oil production, 230% improvement of the NPV over the base scenario, with a risk index of only 0.41.
The results of this study demonstrate that the combination of EOR with different operational strategies results in significant benefits compared to the individual scenarios. Analysis of just oil production independent of economics and risk can be misleading. Infill drilling or flooding should no longer be the question. Instead, the question should be how they can be properly combined at various stages of asset life.
In recent years, the oil and gas industry has gained greater operational efficiencies and productivity by deploying advanced technologies, such as smart sensors, data analytics, artificial intelligence and machine learning — all linked via Internet of Things connectivity. This transformation is profound, but just starting. Leading offshore E&P operators envision using such applications to help drive their production costs to as low as $7 per barrel or less. A large North Sea operator among them successfully deployed a low-manned platform in the Ivar Aasen field in December 2016, operating it via redundant control rooms — one on the platform, the other onshore 1,000 kilometers away in Trondheim, Norway. In January 2019, the offshore control room operators handed over the platform's control to the onshore operators, and it is now managed exclusively from the onshore one. One particular application — remote condition monitoring of equipment — supports a proactive, more predictive condition-based maintenance program, which is helping to ensure equipment availability, maximize utilization, and find ways to improve performance. This paper will explain the use case in greater detail, including insights into how artificial intelligence and machine learning are incorporated into this operational model. Also described will be the application of a closed-loop lifecycle platform management model, using the concepts of digital twins from pre-FEED and FEED phases through construction, commissioning, and an expected lifecycle spanning 20 years of operations. It is derived from an update to a paper presented at the 2018 SPE Offshore Technology Conference (OTC) that introduced the use case in its 2017-18 operating model, but that was before the debut of the platform's exclusive monitoring of its operations by its onshore control room.
Computer vision (CV) techniques were applied to X-ray computed tomographic (CT) images of reservoir cores to evaluate their potential for rapidly identifying fractures and other lithologic/geologic characteristics. The analyses utilized feature-labeled CT cross sectional images, themselves distributed submillimeter voxels of density- and atomic number-sensitive CT Numbers, as inputs to a Fully Convolutional Neural Network (FCN) for semantic segmentation of reservoir core features. In FCN, an image is interrogated using a series of sliding windows at various scales to create weighted filters to reduce error between classes in training images. These networks of filter layers were used to assign probabilities of classes, which were upscaled back to the original image dimensions resulting in probabilistic class assignments onto each pixel. FCN model accuracy, defined by its ability to replicate manually-assigned labels in the raw (unannotated) training image stack, was at least 80% and generally improved with the size of the training set. Once the labels were assigned, the underlying feature frequency, orientation, and size were measured in 3D volume reconstructions using algorithms modified from standard image analysis software. This method allowed users to endow a classification model with subject matter knowledge for further, autonomous label prediction. Thus, while initial image annotation was labor intensive, subsequent images were rapidly classified once the model was built. The classified labels were analyzed for abundance, orientation, and size of fractures were calculated to characterize spatial information of these features. FCN combined with fracture labeling improved knowledge capture and automation of fracture identification. Models trained by high quality 3D datasets can greatly reduce the time needed to describe subsequent core. The method demonstrated is not limited to fractures, other lithologic/geologic features could be trained using the same method, which may result in additional efficiencies.