Predicting the properties of reservoirs beyond the wellbore has been the cornerstone of reservoir characterization. The outcome provides the framework for efficient management and optimization of hydrocarbon reservoirs. Proper reservoir characterization affects all reservoir types and all stages during the life of a field. Far-field characterization encompasses seismic, electromagnetic, and other geophysical surveys. This characterization can be facilitated in various configurations such as cross-well or surface-to-wellbore, accomplished while drilling, in open and cased wells, and while producing hydrocarbons.
The SPE Annual Technical Conference and Exhibition (ATCE) brings together thousands of E&P professionals and experts for learning, networking, and collaborating on the collective goals of the industry. A major highlight in 2018 was the Opening General Session focused on "Translating Big Data into Business Results" with key panelists from Shell, Encana, Schlumberger, and Google Cloud. Registration and housing information for the 2019 conference will be available in mid-June.
More than 8,100 E&P professionals gathered in Dallas for the 2018 SPE Annual Technical Conference and Exhibition (ATCE) for three days of learning, networking, and collaborating on the collective goals of the industry. A major highlight was the Opening General Session focused on "Translating Big Data into Business Results" with key panelists from Shell, Encana, Schlumberger, and Google Cloud.
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.
This paper presents a multidomain integrated workflow that combines geophysics, borehole geology, fracture modeling, and petroleum systems analysis for characterization and resource assessment of basement plays. A 3D fracture model is developed by integrating image log interpretation and seismic data to assess the reservoir potential of fractured basement. The 3D fracture modeling is done using the discrete fracture network (DFN) approach with image log interpretation and other fracture drivers serving as the main input. The DFN is upscaled to generate fracture porosity and fracture permeability properties in a 3D grid. The upscaled fracture porosity is used to estimate the petroleum initially in place (PIIP) for the prospects. Multiple 2D petroleum system modeling is performed where large fault throws are identified from seismic interpretation. The petroleum system study helps in identification of areas with most prolific hydrocarbon generation and expulsion centers, which, coupled with the cross-fault juxtapositions, are the main locales of charging for basement reservoir. Further analysis of all the elements of basement play (i.e., source, reservoir, seal, trap, and migration) is done, and prospective areas within the basement play are delineated with high geological chance of success.
Khare, Sameer (Cairn Oil & Gas vertical of Vedanta Limited) | Baid, Rahul (Cairn Oil & Gas vertical of Vedanta Limited) | Prusty, Jyotsna (Cairn Oil & Gas vertical of Vedanta Limited) | Agrawal, Nitesh (Cairn Oil & Gas vertical of Vedanta Limited) | Gupta, Abhishek Kumar (Cairn Oil & Gas vertical of Vedanta Limited)
The objective of the paper is to present the methodology adopted for dual artificial system modeling in Aishwariya field– an onshore oil field located in prolific Barmer Basin, India. This paper presents a conceptual and feasibility study of combination of Jet pump (JP) and Electrical Submersible Pump (ESP) together as means of artificial lift for production enhancement in a well. It discusses the workflow to model a well producing on dual artificial lift (ESP producing in combination with Jet-Pump) via industry standard software and demonstrates the same with a successful case study.
Requirement of ESP change outs to restore/enhance well production in cases such as undersized pumps, pump head degradation requires an expensive work-over. However, an option for secondary additional lift (JP) installation along with primary lift (ESP) in completion system can eliminate the costly wok-over requirement if both lifts can operate simultaneously.
The procedure to model the dual artificial lift (JP and ESP) has two major components: a) Psuedo IPR at ESP discharge node and b) Standard JP modeling using pseudo IPR. Pseudo IPR is generated by modifying well specific IPR using ESP pump curve for a specific frequency. The down-hole ESP pump intake & discharge pressure sensors help calibrate the model accurately for further prediction.
The existing completion in the Aishwariya field is ESP completion with the option of JP installation in cases of ESP failures as contingency. Moreover, jet pump can be installed using slick line with minimum well downtime (∼ 6 hrs). Therefore, installing and operating the Jet pump above a running ESP will not only increase the drawdown but will result in production enhancement with minimal cost.
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.
The significant temperature difference between the fractured and non-fractured regions during the stimulation fluid flow-back period can be very useful for fracture diagnosis. The recent developments in downhole temperature monitoring systems open new possibilities to detect these temperature variations to perform production logging analyses. In this work, we derive a novel analytical solution to model the temperature signal associated with the shut-in during flow-back and production periods. The temperature behavior can infer the efficiency of each fracture. To obtain the analytical solution from an existing wellbore fluid energy balance equation, we use the Method of Characteristics with the input of a relevant thermal boundary condition. The temperature modeling results acquired from this analytical solution are validated against those from a finite element model for multiple cases.
Compared to the warm-back effect in the non-fractured region after shut-in, a less significant heating effect is observed in the fractured region because of the warmer fluid away from the perforation moving into the fracture (after-flow). Detailed parametric analyses are conducted on after-flow velocity and its variation, flowing, geothermal, and inflow temperature of each fracture, surrounding temperature field, and casing radius to investigate their impacts on the wellbore fluid temperature modeling results.
The inversion procedures characterize each fracture considering the exponential distribution of temperature based on the analytical solutions in fractured and non-fractured regions. Inflow fluid temperature, surrounding temperature field, and after-flow velocity of each fracture can be estimated from the measured temperature data, which present decent accuracies analyzing synthetic temperature signal. The outputs of this work can contribute to production logging, warm-back, and wellbore storage analyses to achieve successful fracture diagnostic.
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.