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.
Downhole control devices are being widely implemented in fields globally; and, because of the costs involved in their implementation, a robust reservoir performance forecast is necessary. A prerequisite to a sound reservoir development plan is to have a robust history-matched reservoir simulation model. This study involves use of a downhole inflow control device (ICD) well configuration in the reservoir simulation model to perform history matching of a green-field offshore Abu Dhabi. The results of this approach are compared to the results from traditional approaches. The scope of this study is to examine the differences in both history match approaches.
Reservoir A is one of the major reservoirs of a green-field located offshore Abu Dhabi, and is being developed with a five-spot water injection pattern. The producers and water injectors are horizontal wells, which are drilled across different flow units within the reservoir. Because the reservoir is heterogeneous across all the flow units, the injection pattern results in a non-uniform water front. The conventional approach to history matching the well performance is to implement a positive skin factor across the well completions to mimic the effect of the inflow control devices (ICDs) installed in the well: increasing the pressure drop (ΔP) between the formation and the well tubing. In this study, the actual downhole configuration was prepared using well-completion analysis software, followed by use of a next-generation reservoir simulator to run the full field reservoir model for the history matching period.
As the field is being developed on the principles of digital concept, continuous high-frequency downhole pressure data is available in flowing as well as shut-in conditions. The use of this data, coupled with direct modeling of the ICDs in the simulation model, resulted in a significant improvement in the reliability of the history match, as compared to traditional approaches.
This study compares two history matching approaches for fields with wells completed with downhole control devices. The core purpose of this study is to integrate the principles of the digital oil field with conventional history matching techniques, with the ultimate goal of improving the history match.
Numerous carbonate reservoir discoveries were made in Indonesia (
The process involves multiple cycles—from formation evaluation (e.g., geomechanics analysis, design of an effective fracturing method, and production forecasting) through the economic impact to the operator. During the early phase of this integrated study, the uncertainties of all static and dynamic parameters (i.e., geological complexity, rock physics, and stress profile) were considered for fracturing design. Production performances from multiple fracturing stimulation scenarios were then modeled and compared to select the plan that optimizes production for the Berai Formation.
Results demonstrated an effective multidiscipline approach toward a comprehensive strategy to meet the ultimate objective in optimizing production. This project leveraged formation evaluation and fracturing design to deliver integrated solutions from exploration to accurate production forecast. The well stimulations were performed by carefully selecting fluid characteristics based on geological-petrophysical properties, pressure, and stress profiles within the area. Results yielded excellent production gains—for the best case, up to 50% with an average of 40% in comparison with initial production by using an acid that provides optimum fracture geometry and permeability.
This opportunity demonstrated the importance of understanding formation behavior and the parameters that aid the selection of an appropriate fracturing design for a low porosity/permeability carbonate reservoir.
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.
This course discusses the fundamental sand control considerations involved in completing a well and introduces the various sand control techniques commonly used across the industry, including standalone screens, gravel packs, high rate water packs and frac-packs. It requires only a basic understanding of oilfield operations and is intended for drilling, completion and production personnel with some sand control experience who are looking to gain a better understanding of each technique’s advantages, limitations and application window for use in their upcoming completions.
Video images have traditionally provided intuitive visual analysis in a wide range of wellbore diagnostic situations. Step changes in computer vision techniques and image processing have led to the ability to make measurements from images (visual analytics). This paper demonstrates several applications where the application of this new data analytics source, combined with state-of-the-art acquisition technology, have further improved understanding of complex well issues while reducing operational time, risk and cost. Examples include hydraulic fracturing, well integrity, erosion, restrictions and leaks. The paper will describe the methods and process of this visual analytics technique through discussion of the three main work flow stages from data acquisition to final analytical product, including the innovative developments in sensor, system and computer vision applications that support each step: 1. Acquisition of full circumferential, depth-synchronized video data of the wellbore. An array of four orthogonally positioned cameras, pointing directly at the pipe wall, concurrently record overlapping images, enabling a continuous full-well video dataset to be obtained.
Recently two multilateral horizontal wells have been completed offshore using dedicated multistage hydraulic fracturing completions. The first well, located in the Central North Sea (referred to as ML-CNS), was stimulated using acid fracturing; while the second well, located in the Black Sea (referred to as ML-BKS), was stimulated using proppant fracturing. This paper presents the different drivers, challenges and lessons learned for each well while emphasizing the well construction and stimulation methodologies developed for the different reservoirs and field characteristics.
The field development drivers for drilling and completing these offshore hydraulic fractured multilateral wells, a first of their kind globally, was different for each case. The objective of the first project, initially considered uneconomic, was to engineer a technical solution for completion and production of two separate reservoirs with only one subsea well. The second project was seeking to optimize infill drilling from the last available slot on the offshore platform to maximize reservoir contact and production in the same reservoir. ML-CNS was a TAML Level 2 completion with a 14-stage, 5 ½" multistage completion run in each lateral and set-up for sequential acid fracturing. Operationally, the first lateral was drilled and stimulated, followed by the drilling and stimulation of the second lateral, using the drilling whipstock to navigate through the multilateral junction. ML-BKS was a TAML Level 3 completion that had a 6-stage, 4 ½" multistage completion installed in each lateral, which were proppant fractured following a sequence designed to minimize the jack-up rig time required. Both legs were drilled and completed prior to starting the stimulation, access to either lateral was achieved with the existing workover unit on the platform by manipulating a custom designed BHA.
The lessons learned from the first project executed in the North Sea were able to be transferred and applied to the second project in the Black Sea to allow for a more efficient and confident completion solution. Led by varying economical and regional constraints, the key factor for both wells centered on delivering operationally simple and reliable multilateral completion designs to economically meet the field development strategy in place.
To the knowledge of the authors and following subsequent literature research, both wells are a worldwide first for an offshore multilateral well completed with multistage acid fracturing and multistage proppant fracturing, and together they represent a new trend in cost-effective offshore field development through well stimulation. The successful case studies for both wells with the combined analysis of the benefits, challenges, and lessons learned will provide a guide and instill confidence with operators who find this approach beneficial with a view to applying it in other assets.
Over 20 percent of major oil and gas (O&G) incidents reported within the European Union (EU) since 1984 have been associated with corrosion under insulation (CUI) [
Using bayesian networks (BNs) Oceaneering has developed a decision support system for effective CUI risk management. The Bayesian model can be incorporated into existing risk-based assessment (RBA) systems. A key feature of the model is the ability to predict corrosion hotspots while quantifying uncertainties. The model uses probabilities based on objective data as well as subject matter expertise, which makes analytical techniques in business accessible to a wide range of users.
With a case study we illustrate how BNs can be used to assess the risk of a fuel gas line on a live asset in the North sea. The most likely estimated remaining life (ERL) is forecasted in the range of 13 to 24 years, with a worst case of 6.7 years and best case of 40 years. By comparison, the customer CUI tracker reported an ERL of 9.7 years. BNs increase flexibility for scheduling inspection intervals, enabling more targeted inspection planning. This is a significant advancement from current RBA methodologies.
The well discussed in this paper has a history of sand production and has exhibit long cyclic slugging behavior with a frequency of several days and reduced average production. The lower completion has a 2000-ft gap between the mule shoe and the packer that is exposed to the larger diameter of 7-in. liner. It is not fully understood whether the slugging is caused by the gap at the lower completion or by sand transportation or both.
Dynamic wellbore modelling with sand particle transport is essential to model the abovementioned complex slugging behavior. A stepwise approach was adopted to allow systematic evaluation of this complex slugging phenomenon. Initially, a lumped inflow with no sand transportation was assumed. In the next stage, sand transportation was included with zonal inflow details added. Several sensitivities on sand particle sizes, particle density, zonal productivity index, etc. were carried out, all of which were aimed at reproducing the long cyclic slugging behavior observed in the field.
Transient simulations successfully produced the slugging behavior observed in the field. Cyclic slugging was seen to be caused by the flow dynamics generated by particles of small to medium size. Some of the key findings were complete blockage by porous sand stationary bed at the lower completion gap (with subsequent pressure buildup), transition from stationary bed to moving bed, rate-dependent velocity of a slow-moving particle bed (eventually producing to surface), and fresh sand particle production from the reservoir at increased drawdown. Measured data from the sand detector confirmed the production of sand, particularly around the same period as predicted by simulation.
Potential slug mitigation solutions were established that should help to achieve higher and stable production. One solution was to achieve higher flow velocity and therefore enable sand transportation as a continuous moving bed (i.e., no blockage), such as reducing the gap size at the lower completion section together with either tubing size reduction or electric submersible pump (ESP) installation. The other solution was to implement an appropriate sand control/sand consolidation method.
Sand production is a common flow assurance issue and sometimes can result in unstable flow behavior causing reduced production. This work is the first attempt to implement particle transport modelling in transient multiphase flow simulation to successfully address a slugging issue in a real well. The analysis helped in understanding the mechanism causing the slugging and arriving at a potential mitigation solution. Further, it provides a step-by-step workflow and a template to address such problems.
It is often stated that necessity is the mother of invention. Never is this proverb more relevant than in the offshore oil and gas environment we currently operate in where real step changes leading to reduced capital and operational expenditure opportunities are sought and embraced by field operators. This paper discusses the pre-job planning, field execution and lessons learned from one such technology that challenged conventional thinking of sand faced completion, casedhole completion and well integrity to successfully deliver a single-trip, interventionless, sand control completion in deepwater Bonga Field, located on the continental slope of the Niger Delta.
Convention dictates that the vast majority of offshore completions be run in two and sometimes three trips which routinely takes in excess of eight to ten days to deploy. Given the day rate of high specification rigs capable of drilling in deep water environments, the ability to reduce this time was deemed paramount to the economics of the project. Utilizing a collaborative approach to initial concept design, risk assessment, extensive testing and contingency planning at component and system level, a single-trip, interventionless, sand control completion system was designed and successfully installed. This paper describes the completion architecture, operational sequence and challenges leading to the installation of an interventionless completion.
A clearly defined set of deliverables and design principles were drawn up to guide the direction of the project including: successfully deploying the upper and lower completion in one trip, and testing all barriers. Adopting a simple, low risk and high reward design, meeting clients well barrier requirements and utilizing proven cost-effective technology are examples of design principles used. The system was tested and evolved through a number of iterations in an onshore trial well environment on a number of occasions leading to the first successful deployment completed in the second half of 2018, resulting in an average completion installation time of 5 days, versus the average 10 days for deploying multi-trip completions. Details of the successful installations, lessons learned, along with planned future activity are outlined within the body of this paper. While several of the components incorporated in the single-trip system had been run previously in isolation, this paper also discusses the steps taken to facilitate the first full-system approach to the application of radio frequency identification (RFID) enabled tools in the first single-trip, interventionless sand control completion system. Several components within the completion have been equipped with this technology including a multi-cycle ball valve, wire wrapped screens fitted with inflow control device (ICD), remote operated sliding sleeve for annular fluid displacement.