Delivering Pressure Transient Analysis During Drawdown on ESP Wells: Case Studies and Lessons Learned

While pressure transient analysis (PTA) is a proven interpretation technique, it is mostly used on buildups because drawdowns are difficult to interpret. However, the deferred production associated with buildups discourages regular application of PTA to determine skin and identify boundary conditions. Several case studies are presented covering a range of well configurations to illustrate how downhole transient liquid rate measurements with electrical submersible pump (ESP) gauges enable PTA during drawdown and therefore real-time optimization. The calculation of high-frequency transient flow rates using ESP gauge real-time data is based on the principle that the power absorbed by the pump is equal to that generated by the motor. This technique is independent of fluid specific gravity and therefore is self-calibrating with changes in water cut and phase segregation. Analytical equations ensure that the physics is always respected, thereby providing the necessary repeatability. The combination of downhole transient high-frequency flow rate and permanent pressure gauge data enables PTA using commonly available analytical techniques and software, especially because superposition time is calculated accurately. The availability of continuous production history brings significant value for PTA. It makes it possible to perform history matching and to deploy semilog analysis using an accurate set of superposition time functions. However, the application of log-log analysis techniques is usually more challenging because of imperfections in input data such as noise, oversimplified production history, time-synchronization issues, or wellbore effects. These limitations are solved by utilizing high-frequency downhole data from ESP. This is possible first as superposition time is effectively an integral function, which dampens any noise in the flow rate signal. Another important finding is that wellbore effects in subhydrostatic wells are less impactful in drawdowns than in buildups where compressibility and redistribution can mask reservoir response. Key reservoir properties, in particular mobility, can nearly always be estimated, leading to better skin factor determination even without downhole shut-in. Finally, with the constraint of production deferment eliminated, drawdowns can be monitored for extended durations to identify boundaries and to perform time-lapse interpretation more efficiently. Confirming a constant pressure boundary or a change in skin enables more effective and proactive production management. In all cases considered, a complete analysis was possible, including buildup and drawdown data comparison. With the development of downhole flow rate calculation technology, it is now possible to provide full inflow characterization in a matter of days following an ESP workover, without any additional hardware or staff mobilization to the wellsite and no deferred production. More importantly, the technique provides the necessary information to diagnose the cause of underproduction, identify stimulation candidates, and manage drawdown.

Data Driven Solution for Enhancing Workover Intervention Activities

Recent advancements in the field of natural language processing (NLP) and machine learning has allowed for the potential to ingest decades of field history and heterogeneous production records. This paper proposes an analytics workflow that leverages artificial intelligence to process thousands of historical workover reports (handwritten and electronic), extract important information, learn patterns in production activity, and train machines to quantify workover impact and derive best practices for field operations. Natural language processing libraries were developed to ingest and catalog gigabytes of field data, identify rich sources of workover information, and extract workover and cost information from unstructured reports. A clustering based architecture was developed and trained to categorize documents based on free text describing the activities found in reports. This machine learning model learnt the pattern and context of repeating words and was able to cluster documents with similar content together. This enabled the user to find a category of documents e.g. workover intervention reports instantaneously. Statistical models were built to determine return on investment from workovers and rank them based on production improvement and payout time. Today, 80% of an oilfield expert's time can be spent manually organizing data. When processing decades of historical oilfield production data spread across both structured (production timeseries) and unstructured records (e.g., workover reports), experts often face two major challenges: 1) How to rapidly analyze field data with thousands of historical records. 2) How to use the rich historical information to generate effective insights to take the proper actions to optimize production. In this paper, we analyzed multiple field datasets in a heterogeneous file environment with 20 different file formats (PDF, Excel, and other formats), 2,000+ files, production history spanning 50+ years across, and 2,000+ producing wells. Libraries were developed to extract files from complex folder hierarchies, machine learning architectures assisted in finding the workover reports from the myriad documents. Information from reports was extracted through Python libraries and optical character recognition technology to build master data source with production history, workover and cost information. The rich dataset was then used to analyze episodic workover activity by well and compute key performance indicators (KPIs) to identify well candidates for production enhancement. The building blocks included quantifying production upside and calculating return of investment for various workover classes. O&G companies have vast volumes of unstructured data and use less than 1% of it to uncover meaningful insights about field operations. Our workflow describes a methodology to ingest both structured and unstructured documents, capture knowledge, quantify production upside, understand capital spending, and learn best practices in workover operations through an automated process. This process helps optimize forward operating expense (OPEX) plans with a focus on cost reduction and shortened turnaround time for decision making.

Carbonate Karstified Oil Fields Geological Prediction and Dynamic Simulation Through Equivalent Relative Permeability Curves

Karst systems heterogeneity may become a nightmare for reservoir modelers in predicting presence, spatial distribution, impact on formation petrophysical characteristics, and particularly in dynamic behaviour prediction. Moreover, the very high resolution required to describe in detail the phenomena does not reconcile with the geo-cellular model resolution typically used for reservoir simulation. The scope of the work is to present an effective approach to predict karst presence and model it dynamically. Karst presence recognition started from the analysis of anomalous well behaviour and potential sources of precursors (logs, drilling evidence, etc.) to derive concepts for karst reservoir model. This first demanding step implies then characterizing each cell classified as karstified in terms of petrophysical parameters. In a two-phase flow, karst brings to fast travelling of water which leaves the matrix almost unswept. This feature was characterized through dedicated fine simulations, leading to an upscaling of relative permeability curves for a single porosity formulation. The workflow was applied to a carbonate giant field with a long production history under waterflood development. Firstly, a machine learning algorithm was trained to recognize karst features based on log response, seismic attributes, and well dynamic evidence, then a karst probability volume was generated and utilized to predict the karst presence in the field. Karst characterization just in terms of porosity and permeability is sufficient to model the reservoir when still in single phase, however it fails to reproduce observed water production. Karst provides a high permeability path for water transport: classical history match approaches, such as the introduction of permeability multipliers, proved to be ineffective in reproducing the water breakthrough timing and growth rate. In fact, the reservoir consists of two systems, matrix, and karst: however, the karst is less known and laboratory analysis shows relative permeability only for the matrix medium. The introduction of equivalent or pseudo-relative permeability curves, accounting for both the media, was crucial for correct modelling of the reservoir underlying dynamics, allowing a proper reproduction of water breakthrough timing and water cut (WCT) trends. The implementation of a dedicated pseudo relative permeability curve dedicated to karstified cells allowed to replicate early water arrival, thus bringing to a correct prediction of oil and water rates, also highlighting the presence of bypassed oil associated with water circuiting, particularly in presence of highly karstified cells.

Miscible WAG Efficiency Assessment on a Large Mature Carbonate Field

This paper is based on the analysis of miscible WAG for an onshore Middle-East field, with strongly undersaturated light oil. Water Alternate Gas operations have been ongoing for around 5 years, which is relatively recent compared to more than 40 years of production history. Goal of this work was to assess the efficiency of this miscible hydrocarbon WAG and to optimize it on the different compartments, with respect to miscibility, voidage replacement, and recycling. As this is a large mature field, with WAG operations dispatched on around 50 injectors and 9 fault blocks (compartments), the method of analysis had to be robust with respect to the different injection strategies followed in the past. It was essentially based on injection and production data, but also used pressure data when available. We computed the following dimensionless variables: oil recovery factor, BSW, voidage replacement ratio (VRR), and also WAG ratio and gas recycling ratio (GRR). Their evolution versus time was analyzed and compared between fault blocks. Using dimensionless variables allowed to compare fault blocks with different initial volumes in place, and to illustrate trends versus time. It was also found beneficial to lump some compartments, when communication was substantiated by pressure data. On the production side, we used the conventional BSW and GOR variables to quantify the water and gas recycling ratio. On the injection side, we observed that in some compartments, the historical WAG ratio was too low in the oil zone, which could be quantified by excluding the peripheral water injection volumes. The analysis allowed also to estimate the gas utilization factor and efficiency, which confirmed the overall high efficiency of miscible gas injection in 3-phase mode. It was also found that the injected fluid efficiency correlated with geology: gas injection tends to be more efficient in zones with high permeabilities at the bottom (coarsening downwards), while water injection is better adapted to zones with high permeabilities at the top (coarsening upwards). Estimating these water and gas efficiencies also allowed to optimize the injection strategy on a field level, by comparing the water efficiency with other units of the field only under waterflood.

Accelerating Mature Field EOR Evaluation Using Machine Learning Uncertainty Workflows Integrating Subsurface And Economics

Field re-development planning for tertiary recovery projects in mature fields traditionally involves a comprehensive subsurface evaluation circle, including static/dynamic modeling, scenario assessment and candidate selection based on economic models. The aforementioned sequential approach is time-consuming and includes the risk of delaying project maturation. This work introduces a novel approach which integrates subsurface geological and dynamic modeling as well as economics and uses machine learning augmented uncertainty workflows to achieve project acceleration. In the elaborated enhanced oil recovery (EOR) evaluation process, a machine learning assisted approach is used in order to narrow geological and dynamic parameter ranges both for model initialization and subsequent history matching. The resulting posterior parameter distributions are used to create the input models for scenario evaluation under uncertainty. This scenario screening comprises not only an investigation of qualified EOR roll-out areas, but also includes detailed engineering such as well spacing optimization and pattern generation. Eventually, a fully stochastic economic evaluation approach is performed in order to rank and select scenarios for EOR implementation. The presented workflow has been applied successfully for a mature oil field in Central/Eastern Europe with 60+ years of production history. It is shown that by using a fully stochastic approach, integrating subsurface engineering and economic evaluation, a considerable acceleration of up to 75% in project maturation time is achieved. Moreover, the applied workflow stands out due to its flexibility and adaptability based on changes in the project scope. In the course of this case study, a sector roll-out of chemical EOR is elaborated, including a proposal for 27 new well candidates and 17 well conversions, resulting in an incremental oil production of 4.7MM bbl. The key findings were: A workflow is introduced that delivers a fully stochastic economic evaluation while honoring the input and measured data.The delivered scenarios are conditioned to the geological information and the production history in a Bayesian Framework to ensure full consistency of the selected subsurface model advanced to forecasting.The applied process results in substantial time reduction for an EOR re-development project evaluation cycle.

‘SKAM Italia did it again’. The multiple lives of a format adaptation from production to audience experience

SKAM Italia, the Italian version of the Norwegian SKAM format, was first released in Spring 2018 and has reached its fourth season in May 2020, overcoming several industrial struggles and slowly engaging an increasing, young and local audience. This article aims to integrate a production–distribution and reception perspective, showing how they mutually reinforce the series’ identity: the peculiarity of SKAM’s distribution system fostered the audience’s engagement, until its complicated production history brought the series to a more conventional circulation. Mutual exchanges between professionals and audiences on a global level are always counterbalanced by national negotiations, influencing the series’ life cycle.

The Implementation of Critical Gas Rate in Liquid Loading Well and Optimization Analysis using the Adequacy Chart

In the gas well, liquid loading occurs when the gas rate is insufficient to lift liquids into the surface such as water and/or condensate. This causes an accumulation of the liquid in the wellbore, supplies additional backpressure to the formation, and may completely kill the well. Meanwhile, the limited space and typically high cost of offshore operations have made a proper study for optimization selection very essential. The selected project must fulfill several requirements, namely: 1) Fit for the purpose, 2) Low risk and uncertainties, and 3) Economic. Hence, this study will describe the pilot project and continuous improvement process of lowering the gas well pressure using a wellhead compressor and a temporary separator to optimize the liquid loading. It also explains the implementation of critical gas rate in predicting the liquid loading event from the well’s production history. A new analysis method utilizing the adequacy chart was proposed to verify the suitability of the available pressure-lowering system unit available in the market with the well candidates. An adequacy chart was constructed from the well’s deliverability, critical gas rate, and lowering pressure unit or system capacity. These three charts will combine to generate an overlapping area, which signifies suitability for the recommended operation. The well’s production data history can be used to predict the liquid loaded-up event due to the continued decline of the generated gas. Also, a combination of the critical gas rate and decline analyses can predict potential liquid loading problems.

Pemanfaatan Data Mining Dalam Memprediksi Produksi Pada PT Pupuk Sriwidjaja Palembang Menggunakan Algoritma Regresi Liniear Berganda

PT.Pupuk Sriwidjaja Palembang is a company that has the main task in the production and marketing of urea fertilizer and npk fertilizer. Production estimates are needed so that companies can determine strategies and breathrought so that the production process runs optimally. This study discusses the application of data mining in predicting the production of NPK Fertilizer Formula 15-15-15 PSO at PT.Pupuk Sriwidjaja Palembang. In its application, this research uses the multiple Linear Regression Algorithm by utilizing production history data for the 2019-2020 period. If you have already mined the production history data for the 2019-2020 period, there will bi predictions in 2021 which are predicted by the rapidminer application and using a multiple linear regression algorithm, which produces a predicted value of fertilizer production in 2021 as much as 70800 tons.

Microbial Communities of Ascocarps and Soils in a Natural Habitat of Tuber Indicum

Truffles are the fruiting bodies of hypogeous fungi in the genus Tuber. Some truffle species usually grow in an area devoid of vegetation, called brûlé, which knowledge about the microbial composition and structure is still limited. Here, we investigated the bacterial and fungal communities of Tuber indicum ascocarps and soils inside and outside a characteristic brûlé from a poplar plantation with no truffle production history in northeastern China using a high-throughput sequencing approach. A predominance of bacterial phylum Proteobacteria was observed in all samples, with Bacillus among the main genera in the ascocarps, while members of Lysobacter and unidentified Acidobacteria were more abundant in the soil. In addition, Gibberella, Fusarium and Absidia were the dominant fungi in the ascocarps, while Tuber were enriched in the ascocarps and soils inside the brûlé. Soil samples from inside the brûlé had a lower bacterial diversity and a greater fungal diversity than did those from outside the brûlé. Furthermore, some mycorrhization helper bacteria (Rhizobium) and ectomycorrhiza-associated bacteria (Lysobacter) were detected, indicating their potential roles in the complex development of underground fruiting bodies and brûlé formation. These findings may contribute to the protection and cultivation of truffles.

“A Panorama of Gay Life”: Nighthawks and British Queer Cinema in the 1970s

This chapter details the production history and reception of Paul Hallam and Ron Peck’s film Nighthawks (1978), often recognized as a “classic” of British LGBTQ cinema. It centrally engages with Vito Russo’s suggestion that the film offered “a community reaction to itself.” Making the film was a lengthy undertaking: the chapter draws on Peck and Hallam’s archives to reconstruct its creation, and unpacks Peck’s involvement with the Four Corners collective and its influence on the content and form of Nighthawks. The film is situated in relation to key events in British queer history and the landscape of British filmmaking during the decade, as well as in relation to Richard Dyer’s landmark 1977 film season “Images of Homosexuality.” The film’s “sequel,” Strip Jack Naked (1991), is also explored as a partial atonement for Nighthawks’s omissions.