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Collaborating Authors
DrillingInfo
Data-driven Approach to Quantify Oilfield Water Lifecycle and Economics in the Permian Basin
Sharma, Akash (DrillingInfo) | Thomasset, Ian (DrillingInfo)
Abstract Oilfield water management has become an increasingly critical aspect of oil and gas operations in the United States. With the generational changes in completion techniques making the frac jobs bigger and more resource intensive, proper water management and utilization is key in optimizing operations. At a high level, drought, municipal non-potable sources, produced water volumes, seismicity, SWD capacity, larger frac jobs, capital expense amongst others have drastically increased the considerations for efficient water management. With about 30% of the active North American fleets in the Permian, the issue has become particularly acute regionally. This is driven by the increasing requirements of hydraulic fracturing as an average US well in 2017 used around 9.8 million gallons of water for a frac job. In addition, produced and flowback water from oil and gas wells is an increasing liability for operators in active fields which can create treatment and logistics challenges. The present paper combines data from a wide variety of sources and looks at the dynamic of produced water, frac water and disposed/injected water for operations. It provides a solid mass balance assessment of the water going in and out of the oil field. The paper also overlays several of the known trends to identify opportunities for efficiency gains as well as potential “cost crisis”. This allows for a more robust understanding of economic impact of the water management. Identifying these opportunities, the paper examines formation water chemistry trends and combines them with best practices to provide best practices for water treatment to impact operational efficiencies now as well as projects it in the future. Overall, there is a clear increase in volume of produced water in these major oil producing regions, with the Delaware Basin alone increasing significantly (~80% increment in produced water since 2015) year on year since the emergence of horizontal drilling. The analysis also showed the impact on parallel industries like water midstream infrastructure and logistics development. Cost optimization has driven companies to take on more comprehensive projects with 50% or higher reductions in cost switching from trucking to pipeline.
- North America > United States > New Mexico (1.00)
- North America > United States > Texas > Reeves County (0.28)
- Research Report (0.47)
- Overview (0.46)
- Geology > Structural Geology > Tectonics > Plate Tectonics > Earthquake (0.48)
- Geology > Geological Subdiscipline > Geochemistry (0.46)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > North America Government > United States Government (0.95)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.69)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (29 more...)
Abstract Eagle Ford shale in South Texas is a major oil and gas production play of in the US Gulf Coast region. While some attribute the successful well performance of Eagle Ford to the technology advancement such as horizontal drilling and hydraulic fracturing, others credit the role of geological settings. However, it is still unclear what the individual or combined effects from these two sides are. Data-driven approaches, including Partial Least Square (PLS), Random Forest (RF), and Deep Neural Network (DNN), reveal relationships among the production, geological settings, and completion strategies. In this study, we considered six-month cumulative oil production as the well performance criterion for horizontal wells completed from 2015 to 2017. We selected completion parameters such as perforation length, proppant loading, and fluid volume. We selected structural depth, lower Eagle Ford Shale thickness, total organic carbon (TOC), number of limestone beds, and average bed thickness as the key geological controls on regional production. We calculated Spearman correlation coefficients to detect correlated input parameters and applied Singular Value Decomposition (SVD) to identify redundant input parameters. Then we performed partial linear square (PLS) regression to predict the six-month oil production from geological and completion parameters. We then used random forest (RF) and deep neural network (DNN) as non-linear machine learning techniques to predict six-month oil production and compared the prediction accuracies for these techniques against the recorded well performance using the coefficient of determination and mean squared error as criteria. Last, we ranked the relative importance of each input parameter using RF and Minimum Redundancy Maximum Relevance (MRMR). This paper first provides the rational of input variables selection. Then the construed model helps understand the effects of completion designs and geological variables on well productivity in the Eagle Ford. This might provide valuable information to help to make decisions for new well development. This concept can be generalized among other plays.
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.50)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- (10 more...)
Summary Development of shale reservoirs such as the Barnett Shale frequently includes the study of associated geomechanical and rock properties. Considerable effort has been placed into understanding properties that can be obtained from seismic data using inversion such as of ?? and µ?.. In most cases, studies of these properties are driven by theoretical understanding combined with careful analysis or core. In this abstract, we present a data driven approach based examining the statistical properties of ?? and µ? attributes obtained from well logs from the Lower Barnett Shale. Using an unsupervised learning approach to data clustering, we allow our data to speak for themselves, providing insight into the underlying data distribution. We then look at the rock proprieties of the discovered clusters to better understand the nature of the data.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play > Shale Gas Play (0.90)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > United States > Michigan > Michigan Basin > Wise Field > Dundee Limestone Formation (0.91)
- North America > Mexico (0.91)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale oil (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)