Evaluasi Penggunaan Sucker Rod Pump Pada Sumur Rb-36 Rb- 91, Dan Rb-135 Dengan Menggunakan Data Sonolog Dan Dynamometer Untuk Meningkatkan Produksi Di PT Pertamina Ep Asset 1 Field Ramba

Secara umum metode produksi dibagi menjadi dua, yaitu sembur alam (natural flow) dan pengangkatan buatan (artificiallift). Sembur alam merupakan metoda mengalirnya fluida dari zona perforasi ke permukaan sumur secara alamiah, halini disebabkan tekanan reservoir yang mendorong fluida naik ke permukaan masih sangat tinggi. Seiring dengan waktuberproduksi, maka terjadi penurunan tekanan reservoir dan keadaan ini menyebabkan berkurangnya tingkat produksisumur tersebut, maka untuk mengatasi masalah ini dapat dilakukan dengan cara pengangkatan buatan (artificial lift).Adapun tujuan dari artificial lift adalah untuk membantu pengangkatan fluida dari dalam sumur ke permukaan. Salahsatu metode pengangkatan ini, yaitu sucker rod pump. Untuk meningkatkkan produktivitas suatu pompa sucker rod pumpperlu diperhatikan kapasitas produksi pompa,panjang langkah, kecepatan pemompaan maupun letak kedalaman pompa.Sumur RB-36, RB-91, dan RB-135 adalah sumur migas yang terdapat di lapangan Ramba, PT Pertamina EP Asset 1Field Ramba. Berdasarkan analisis kurva IPR Vogel diperoleh laju produksi maksimal (Qmaks) untuk masing-masingsumur, yaitu RB-36 sebesar 612,18 BFPD, RB-91 sebesar 336,18 BFPD dan RB-135 sebesar 306,70 BFPD, sedangkandari data sonolog diperoleh bahwa produksi sumur RB-36 sebesar 485 BFPD, RB-91 sebesar 257 BFPD dan RB-135sebesar 236 BFPD. Berdasarkan hasil analisis dan optimasi yang telah dilakukan diperoleh besar laju produksi yangdapat dicapai sumur RB-36 sebesar 500 BFPD, sumur RB-91 sebesar 283 BFPD dan sumur RB-135 sebesar 270 BFPD.Dari hasil uji dynamometer akan diperoleh data pump card ketiga sumur tersebut. Dari analisis data pump card ketigasumur tu diperoleh bahwa sumur RB-36 mengalami fluid acceleration, sumur RB-91 mengalami kebocoran padatravelling valve, sedangkan untuk RB-135 mengalami kerusakan pada standing valvenya

A Data-Driven Approach For Monitoring And Predictive Diagnosis Of Sucker Rod Pump System

Given its long operational history, a sucker-rod pump (SRP) has been widely utilized as a lifting solution to bring reservoir fluids to the surface with low cost and high efficiency. However, debugging the rod pump issues requires on-site activities that could cost time and money for operators. With the vast dataset collected from years of operation, numerous companies are looking to turn these engineering processes into automated systems in the oilfield network, despite the complexity of data and lack of knowledge. The integral approach is to develop real-time diagnostics for downhole conditions. The emerging artificial intelligence and big-data analytics have provided relatively precise downhole condition forecasting based on available data, enabling better decision-making. This thesis focuses on collecting representative data and utilizing machine learning techniques to predict operational anomalies of sucker-rod pumps. An experimental design at the University of Oklahoma, referred to as Interactive Digital Sucker Rod Pumping Unit (IDSRP), was used to facilitate a data-driven solution and monitor SRP performance and diagnostics. The physical framework includes a 50-ft transparent casing and tubing with a downhole rod pump at the bottom. A linear actuator provides the rod string’s reciprocal movement and simulates different surface units and operating scenarios. This facility uses proper instrumentation and a data acquisition system for signal sensor readings. A workflow is developed to translate surface dynamometer cards to downhole ones and train predictive models in time-driven pressure and rate data. Though primarily focusing on the normal pump operation, the test matrix varies in stroke length, pump speed, and rod movement shape. The tests validate the model to classify and detect various operational conditions in sucker-rod pumps. The model dynamically categorizes the pumps into key states of ideal condition and over-pumping with a regression fit of accuracy higher than 0.7 and overall classification accuracy of 92%. Moreover, the real-time model anticipates an event in which the pump experiences a slight pumping-off that could potentially deteriorate the rod. The results also help understand key features that drive sucker rod pump performance prediction and help detect anomalous pump behavior. The machine learning algorithms, developed by the physics-based inputs, generate predictive models, thus classifying operational conditions or failures of the pump. The diagnosis for the pump’s anomalies is also predicted by a real time analysis. The visualization enables to recognize the patterns and abnormal phases early. The explainable machine learning (i.e. Shapley additive explanation) helps decoding the predictive models with feature importance, local and global sensitivities in categorizing SRP conditions. The developed unit has the capability of working with different well conditions, combining with real-time training and applying models, to initiate early warnings. The developed processes and workflows have the potential of becoming a generic optimizing and monitoring model for rod pumps. The novelty of this setup consists not only in its mechatronic design but also in through monitoring of the pump operations

Case studies in causal inference and anomaly detection

The study of parent-child well interactions in unconventional shales has generated high interest both in the industry and academia over the last decade. This is largely because of the growing number of child wells and their immediate impact on the parent well production owing to several dynamic factors, one of them, including well spacing. Evaluating the impact of well spacing on parent and child well production performance is challenging. Several studies have resorted to geomechanical stress and fracture modeling combined with dynamic simulation techniques while a few operators have chosen field trials to evaluate optimal well spacing. Several data-driven approaches to address the well-spacing problem have also become popular. One such commonly used data-driven approach simply calculates the difference in cumulative production over a specified period of time for parent and child wells grouped by spacing. This approach has been the method of choice for several different recent analyses of well spacing; however, given that the method of simple averages does not account for formation properties or completion design, the results may be compromised and can lead to counterintuitive results. In this thesis, I introduce a new data-driven approach leveraging the power of causal inference as seen in clinical trials for multivariate observational studies. The causal approach addresses the problem behind the routinely used simple averages approach by providing a formalism to control for reservoir and completion variables when evaluating the impact of well spacing on production performance. I apply the causal inference workflow to a dataset from a prolific oil basin in Texas with over 700 wells in the analyses. It includes the formation properties, fluid volume, proppant weight, landing zones and the downhole locations of the wells. Using the causal inference workflow, I evaluate the effect of well spacing on well performance at different parent-child spacing ranges. The optimal well spacing is then estimated for this shale play based on the magnitude of the causal effects. These estimates are then compared with the simple averages approach to demonstrate the power and utility of causality. In the second part of the thesis, I transition into a discussion on anomaly detection approaches applied in the oil and gas industry. I discuss current anomaly detection methods for a widely used artificial lift method – the Sucker Rod Pump (SRP). Today, there is a growing need for fast and accurate anomaly detection systems given the emergence of Internet of Things (IoT) and access to Big Data. Anomaly detection using human operators can be expensive, is often subject to bias and experience-levels and does not scale very well with the need to monitor more than a few tens of wells. With SRPs, the problem of anomaly detection becomes a problem of image classification where dynamometer cards are evaluated for signatures of failure. While this has been the mainstay of anomaly detection for pumpjacks, in this thesis, I automate this task of monitoring and detecting the anomalies from the SRP pump cards. Several thousand synthetic pump cards specific to pump failures modes are generated from the literature and fed to a deep learning model. This deep learning model is a Convolutional Neural Network (CNN) which is commonly used in image classification tasks, speech recognition tasks as well as many other modern-day technology applications including smart phones, self-driving cars, aerospace etc. The CNN used in this work offers a very high accuracy for detecting a variety of pump failures modes thereby offering the potential to save costs, time and unnecessary workovers for the operator