Understanding the principles of fluid flow through the production system is important in estimating the performance of individual wells and optimizing well and reservoir productivity. In the most general sense, the production system is the system that transports reservoir fluids from the subsurface reservoir to the surface, processes and treats the fluids, and prepares the fluids for storage and transfer to a purchaser. Figure 1 depicts the production system for a single well system. Figure 1--Production System and associated pressure losses. The reservoir is the source of fluids for the production system.
Pathak, Varun (Computer Modelling Group Ltd.) | Hamedi, Yousef (Computer Modelling Group Ltd.) | Martinez, Oscar (Computer Modelling Group Ltd.) | Vermeulen, Stephen (Computer Modelling Group Ltd.) | Kumar, Anjani (Computer Modelling Group Ltd.)
Integrated production systems models are very valuable for predicting the performance of complex systems containing multiple reservoirs and networks. In addition, the value of quantifying uncertainty in reservoirs and production systems is immense as it can build confidence in operational investments. However, traditionally it has been extremely tedious to incorporate uncertainty assessments in the context of integrated production systems modelling. This has been addressed in the current work with the help of a case study.
In the current work, a complex integrated production systems model is presented - from Pre-Salt carbonates reservoir offshore of Brazil. The model includes multiple reservoirs with unique fluid types and complex fluid blending in the production network, multiphase and thermal effects in flowlines and risers, gas separation, gas processing, gas compression, and re-injection for either pressure maintenance or for miscible EOR.
The operational strategies, well placement, and well and network configurations are often based on a single geological realization. With the case study presented in this paper, an integrated way of quantifying geological uncertainty has been presented. A new multi-user, multi-disciplinary tool was used for this study that removed any discontinuities and inconsistencies that typically occur in such projects when multiple standalone tools are used for individual tasks. When quantifying uncertainty on production, the dependence on a single realization was eliminated as uncertain parameters were identified and used for creating robust probabilistic forecasts. Probability distribution curves were generated to represent the uncertainty in overall production from this asset, and the risk associated with operational investments was minimized.
Typically, an end-to-end uncertainty assessment is missing from the traditional Integrated Modelling workflows. With this new approach, the challenge of achieving a truly integrated uncertainty assessment for integrated reservoir and production models has been addressed successfully.
Development wells are drilled for different purposes that varies based on the business needs. Some scenarios can be for flowing production, power water injector, gas, or to monitor the performance of a well. Coupled with the technology in wellbore drilling activity and the artifacts that are required for the workflow approval. Through evolving technology, the system captures every step of wellbore activity to address the business needs and streamline the approval process. Data fuels Industry Revolution (IR) 4.0 and effective data analytics is the prerequisite for successful implementation of digital enterprise applications.
Well approval is an in-house developed system embedded in Petrel application for reservoir characterization geologists. It standardizes well approval documents across the organization. The geologists may generate multiple well approval documents in case a change in the plan has occurred. Different scenarios require geologists to replace the plan that reflects the well approval document. Planning is the main pillar in the process of Well Approval, and automating the planning part simplifies the well approval process. The system automates the generation of three critical documents. First, Integrated Requirements (IRs), which consists of logging and threshold values for each hole section generates the reservoir amplitude impedance and porosity maps, and finally allows the geologist to capture seismic maps from 3D geological models. Second, Prognosis geological horizons details. Third, Location Evaluation. The backbone of this system is that it is integrated with different back-end systems such as corporate database, Petrel, Openwork and Geolog. In addition, geologists are not required to select the offset wells. The offset wells algorithm designates the wells for geologies based on specific criteria that is built in the system such as reservoir type and dynamic log selection. The system tackles both environments 2D and 3D based on the giving parameters such as facie. The system is able to detect the environment and prepare the documents accordingly.
Since the system is aligned with the business pattern for well approval process, it significantly improves the time required to create these documents from two to three (2-3) days to less than 1 hour (90%) and the data will be readily available through Real-Time Operations Center Knowledge System (also known as ROCKS) for further analysis and reporting.
Sour production from offshore and land-based wells causes hydrogen sulfide (H2S) release during downhole and topsides operations. Improper handling of H2S can lead to serious environmental and safety concerns as well as numerous corrosion and compliance issues. Consequently, H2S can add significantly to the total cost of well operations. The application of efficient H2S management technologies can reduce environmental and safety concerns, enable the use of lower-cost materials, and comply with H2S specifications. To remove H2S from mixed production applications, several chemistries are commonly used. The most common are triazines, glyoxal, and metal-based chemistries. Although each can be effective to a certain extent, these technologies have issues with efficiency or they can create serious side issues. The reaction of triazines with H2S in mixed production is highly inefficient and it creates scaling. Glyoxals suffer from poor efficiency, thermal instability, and corrosivity. The metal-based chemistries are the most efficient in mixed production, but in certain application regimes they can create serious solids and emulsion issues. These challenges can increase CAPEX and OPEX as well as lead to significant downtime and lost production. To overcome issues with currently used chemistries in mixed sour production, extensive research was conducted to identify chemistry that would efficiently remove H2S while minimizing negative side effects.
Systematic evaluation was performed for a series of chemistries to compare the scavenging efficiency, with a special emphasis on mixed production systems. Focus was also given on studying the associated side effects like emulsification tendency, scaling tendency, etc. to ensure the chemistry had no/minimal side effects seen by the more conventional chemistries. A high-throughput lab technique is presented that was designed to mimic scavenging tendency in sour mixed production environment. A continuous gas flow testing technique that helped study the reaction kinetics is also described.
Laboratory and pre-field results proved the efficacy of the new non-MEA, non-triazine chemistry in mitigating H2S in upstream, midstream and downstream applications while being especially efficient in mixed production systems. Laboratory testing proved the chemistry to be highly efficient compared to triazine in mixed production systems. Results also indicated the chemistry is non-emulsion forming and has very little scaling tendency. Testing conducted in the field demonstrated that the new chemistry cost-effectively removes H2S and meets the operator specifications.
The novel, non-triazine scavenger technology has significantly better performance than triazine, no emulsion concerns, acceptable HSE, non-corrosive effects, and less downstream concern than MEA triazine or metal-based scavengers. The new and differentiated chemistry reduces CAPEX and OPEX, drives productivity, improves reliability and reduces non-productive time.
Carbonate reservoirs are often comprised of a heterogeneous pore system within a matrix of variably distributed minerals including anhydrite, dolomite, and calcite. When describing carbonate thin sections, it is routine to assign relative abundance levels to each of these components, which are qualitative to semi-quantitative (e.g. point-counting) and vary greatly depending on the petrographer. Over the past few decades, image analysis has gained wide use among petrographers, however, thin section characterization using this technique has been primarily limited to the pore space due to the difficulty associated with optical recognition beyond the blue-dyed epoxy associated with the pores. Here, we present a new method of computerized object-based image segmentation (Quantitative Digital Petrography: QDP) that relies on a predefined rule set to enable rapid, automated thin section quantification with only minor human interaction. We have developed a novel work flow that automatically isolates the sample on a high-resolution (i.e. <1μm/pixel) scanned thin section, segments the image, and assigns those segments to predefined categories – e.g., pores, cement, grains, etc. Using this technique, statistically relevant numbers of thin sections can be rapidly processed and quality controlled, thereby allowing quantitative data such as MICP, wettability, and surveillance data to be integrated with the petrographic observations for a more complete description of the carbonate rock. Our technique can also incorporate multiple layers, such as cross-polarization, Back Scatter Electron (BSE) imaging, and elemental maps, which allow additional information to be easily integrated with results from QDP. The QDP approach is a significant improvement over previous digital image analysis methods because it 1) does not require binarization, 2) eliminates the subjectivity in assessing abundance levels, 3) requires less hands-on time for the petrographer, and 4) provides a much fuller dataset that can be incorporated across an entire well or field to better address common challenges associated with carbonate reservoir characterization, such as understanding pore type and cement abundance, pore connectivity, grain distribution, and reservoir flow characteristics.
Subsea Production Systems—Will 2019 Be a Tipping Point? The Golfinho gas field development is among the high-profile projects for which operators agreed to EPC contracts prior to FID. The 286 subsea trees ordered in 2018 was the highest quantity since 2013 and suggests an industry firmly on the right side of a recovery. Increasing orders are a welcome relief for the still-beleaguered subsea supply chain but have yet to translate into meaningful revenue growth. Revenues have remained relatively static over the past 24 months as the growing number of subsea projects hitting the EPCI phase is offset by a transition from high-value pre-2014 backlog to much lower- priced post-downturn contracts.
ABSTRACT Robustness is a system property that reflects its resistance to the initiation of hazardous events and their progression beyond defined limit state thresholds. Under scoped perturbations a robust system should not display disproportionate response or cascading failures. Three levels of robustness are identified: Operational (R1), Survival (R2) and Reserve (R3) that span responses in state space ranging from the safe operating limit (SOL) up to system failure at the ALS limit. Systems engineering methods can be used to define system boundaries then map subsystems and their interactions. HAZIDS can be used to identify critical failure modes for engineering analysis. Risk methods can be used to assess the acceptability of the robustness measure. Robustness is a system property that reflects its resistance to the initiation of hazardous events and their progression beyond defined limit state thresholds. A robust system maintains essential minimum features under scoped perturbations.
Not only that, it’s hard to operate, evolve, and troubleshoot. This post explores how applying the functional programming paradigm to data engineering can bring a lot of clarity to the process. This post distills fragments of wisdom accumulated while working at Yahoo, Facebook, Airbnb, and Lyft, with the perspective of well over a decade of data warehousing and data engineering experience. "In computer science, functional programming is a programming paradigm — a style of building the structure and elements of computer programs — that treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data. It is a declarative programming paradigm, which means programming is done with expressions or declarations instead of statements.
Awan, Kamran (Petroleum Development Oman) | Al Aufi, Mohammed (Petroleum Development Oman) | Al Salti, Hilal (Petroleum Development Oman) | Al Noumani, Hussain (Petroleum Development Oman) | Nabavi, Bijan (Petroleum Development Oman) | Al Ghaithy, Ali (Petroleum Development Oman) | Al Busaidi, Khamis (Petroleum Development Oman) | Al Harrasi, Ali (Petroleum Development Oman) | Al Lamki, Ali (Petroleum Development Oman) | Al Mujaini, Rahima (Petroleum Development Oman) | Al Salhi, Mohammed (Petroleum Development Oman) | Al Nadabi, Seif (Petroleum Development Oman) | Al Abri, Abdulla (Petroleum Development Oman) | Al Zaabi, Yousef (Petroleum Development Oman) | Al Busaidi, Salim (Petroleum Development Oman)
'Sweating the Asset’ is an integrated change management approach for maximizing cheap oil production from existing fields and facilities, without capital expenditure. ‘Sweating the Asset’ utilizes a
The ‘Sweating the Asset’ goal was introduced as an organisational initiative in Q1 2017 with the aim of helping producing assets close their ‘gap to potential’ and operate as closely as possible to technical limit. The approach enables team leaders managing different components of the integrated production system to focus on a common goal and make aligned decisions.
The structured ‘Sweat the Asset’ process integrates components of the company's Lean Management System (LMS), including:
'Sweating the Asset’ has been deployed in 13 production systems within the organisation and currently at different levels of maturity. As an example of goal deployment, an EOR polymer injection facility with suboptimal performance, poor compliance with injection and viscosity requirements had resulted in a severe decline in oil production. In order to safeguard production and close an estimated production gap of 4000 barrels/day, a goal was set to
The plan for 2018 is to ensure that ‘Sweating the Asset’ is fully embedded and sustainable in all assets across the organisation. This may be seen as step change in, and the next level of Wells, Reservoir and Facilities Management (WRFM) maturity. This paper will primarily focus on the Goal Deployment process and strategy.